findings example in qualitative research

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Qualitative Data Analysis

23 Presenting the Results of Qualitative Analysis

Mikaila Mariel Lemonik Arthur

Qualitative research is not finished just because you have determined the main findings or conclusions of your study. Indeed, disseminating the results is an essential part of the research process. By sharing your results with others, whether in written form as scholarly paper or an applied report or in some alternative format like an oral presentation, an infographic, or a video, you ensure that your findings become part of the ongoing conversation of scholarship in your field, forming part of the foundation for future researchers. This chapter provides an introduction to writing about qualitative research findings. It will outline how writing continues to contribute to the analysis process, what concerns researchers should keep in mind as they draft their presentations of findings, and how best to organize qualitative research writing

As you move through the research process, it is essential to keep yourself organized. Organizing your data, memos, and notes aids both the analytical and the writing processes. Whether you use electronic or physical, real-world filing and organizational systems, these systems help make sense of the mountains of data you have and assure you focus your attention on the themes and ideas you have determined are important (Warren and Karner 2015). Be sure that you have kept detailed notes on all of the decisions you have made and procedures you have followed in carrying out research design, data collection, and analysis, as these will guide your ultimate write-up.

First and foremost, researchers should keep in mind that writing is in fact a form of thinking. Writing is an excellent way to discover ideas and arguments and to further develop an analysis. As you write, more ideas will occur to you, things that were previously confusing will start to make sense, and arguments will take a clear shape rather than being amorphous and poorly-organized. However, writing-as-thinking cannot be the final version that you share with others. Good-quality writing does not display the workings of your thought process. It is reorganized and revised (more on that later) to present the data and arguments important in a particular piece. And revision is totally normal! No one expects the first draft of a piece of writing to be ready for prime time. So write rough drafts and memos and notes to yourself and use them to think, and then revise them until the piece is the way you want it to be for sharing.

Bergin (2018) lays out a set of key concerns for appropriate writing about research. First, present your results accurately, without exaggerating or misrepresenting. It is very easy to overstate your findings by accident if you are enthusiastic about what you have found, so it is important to take care and use appropriate cautions about the limitations of the research. You also need to work to ensure that you communicate your findings in a way people can understand, using clear and appropriate language that is adjusted to the level of those you are communicating with. And you must be clear and transparent about the methodological strategies employed in the research. Remember, the goal is, as much as possible, to describe your research in a way that would permit others to replicate the study. There are a variety of other concerns and decision points that qualitative researchers must keep in mind, including the extent to which to include quantification in their presentation of results, ethics, considerations of audience and voice, and how to bring the richness of qualitative data to life.

Quantification, as you have learned, refers to the process of turning data into numbers. It can indeed be very useful to count and tabulate quantitative data drawn from qualitative research. For instance, if you were doing a study of dual-earner households and wanted to know how many had an equal division of household labor and how many did not, you might want to count those numbers up and include them as part of the final write-up. However, researchers need to take care when they are writing about quantified qualitative data. Qualitative data is not as generalizable as quantitative data, so quantification can be very misleading. Thus, qualitative researchers should strive to use raw numbers instead of the percentages that are more appropriate for quantitative research. Writing, for instance, “15 of the 20 people I interviewed prefer pancakes to waffles” is a simple description of the data; writing “75% of people prefer pancakes” suggests a generalizable claim that is not likely supported by the data. Note that mixing numbers with qualitative data is really a type of mixed-methods approach. Mixed-methods approaches are good, but sometimes they seduce researchers into focusing on the persuasive power of numbers and tables rather than capitalizing on the inherent richness of their qualitative data.

A variety of issues of scholarly ethics and research integrity are raised by the writing process. Some of these are unique to qualitative research, while others are more universal concerns for all academic and professional writing. For example, it is essential to avoid plagiarism and misuse of sources. All quotations that appear in a text must be properly cited, whether with in-text and bibliographic citations to the source or with an attribution to the research participant (or the participant’s pseudonym or description in order to protect confidentiality) who said those words. Where writers will paraphrase a text or a participant’s words, they need to make sure that the paraphrase they develop accurately reflects the meaning of the original words. Thus, some scholars suggest that participants should have the opportunity to read (or to have read to them, if they cannot read the text themselves) all sections of the text in which they, their words, or their ideas are presented to ensure accuracy and enable participants to maintain control over their lives.

Audience and Voice

When writing, researchers must consider their audience(s) and the effects they want their writing to have on these audiences. The designated audience will dictate the voice used in the writing, or the individual style and personality of a piece of text. Keep in mind that the potential audience for qualitative research is often much more diverse than that for quantitative research because of the accessibility of the data and the extent to which the writing can be accessible and interesting. Yet individual pieces of writing are typically pitched to a more specific subset of the audience.

Let us consider one potential research study, an ethnography involving participant-observation of the same children both when they are at daycare facility and when they are at home with their families to try to understand how daycare might impact behavior and social development. The findings of this study might be of interest to a wide variety of potential audiences: academic peers, whether at your own academic institution, in your broader discipline, or multidisciplinary; people responsible for creating laws and policies; practitioners who run or teach at day care centers; and the general public, including both people who are interested in child development more generally and those who are themselves parents making decisions about child care for their own children. And the way you write for each of these audiences will be somewhat different. Take a moment and think through what some of these differences might look like.

If you are writing to academic audiences, using specialized academic language and working within the typical constraints of scholarly genres, as will be discussed below, can be an important part of convincing others that your work is legitimate and should be taken seriously. Your writing will be formal. Even if you are writing for students and faculty you already know—your classmates, for instance—you are often asked to imitate the style of academic writing that is used in publications, as this is part of learning to become part of the scholarly conversation. When speaking to academic audiences outside your discipline, you may need to be more careful about jargon and specialized language, as disciplines do not always share the same key terms. For instance, in sociology, scholars use the term diffusion to refer to the way new ideas or practices spread from organization to organization. In the field of international relations, scholars often used the term cascade to refer to the way ideas or practices spread from nation to nation. These terms are describing what is fundamentally the same concept, but they are different terms—and a scholar from one field might have no idea what a scholar from a different field is talking about! Therefore, while the formality and academic structure of the text would stay the same, a writer with a multidisciplinary audience might need to pay more attention to defining their terms in the body of the text.

It is not only other academic scholars who expect to see formal writing. Policymakers tend to expect formality when ideas are presented to them, as well. However, the content and style of the writing will be different. Much less academic jargon should be used, and the most important findings and policy implications should be emphasized right from the start rather than initially focusing on prior literature and theoretical models as you might for an academic audience. Long discussions of research methods should also be minimized. Similarly, when you write for practitioners, the findings and implications for practice should be highlighted. The reading level of the text will vary depending on the typical background of the practitioners to whom you are writing—you can make very different assumptions about the general knowledge and reading abilities of a group of hospital medical directors with MDs than you can about a group of case workers who have a post-high-school certificate. Consider the primary language of your audience as well. The fact that someone can get by in spoken English does not mean they have the vocabulary or English reading skills to digest a complex report. But the fact that someone’s vocabulary is limited says little about their intellectual abilities, so try your best to convey the important complexity of the ideas and findings from your research without dumbing them down—even if you must limit your vocabulary usage.

When writing for the general public, you will want to move even further towards emphasizing key findings and policy implications, but you also want to draw on the most interesting aspects of your data. General readers will read sociological texts that are rich with ethnographic or other kinds of detail—it is almost like reality television on a page! And this is a contrast to busy policymakers and practitioners, who probably want to learn the main findings as quickly as possible so they can go about their busy lives. But also keep in mind that there is a wide variation in reading levels. Journalists at publications pegged to the general public are often advised to write at about a tenth-grade reading level, which would leave most of the specialized terminology we develop in our research fields out of reach. If you want to be accessible to even more people, your vocabulary must be even more limited. The excellent exercise of trying to write using the 1,000 most common English words, available at the Up-Goer Five website ( https://www.splasho.com/upgoer5/ ) does a good job of illustrating this challenge (Sanderson n.d.).

Another element of voice is whether to write in the first person. While many students are instructed to avoid the use of the first person in academic writing, this advice needs to be taken with a grain of salt. There are indeed many contexts in which the first person is best avoided, at least as long as writers can find ways to build strong, comprehensible sentences without its use, including most quantitative research writing. However, if the alternative to using the first person is crafting a sentence like “it is proposed that the researcher will conduct interviews,” it is preferable to write “I propose to conduct interviews.” In qualitative research, in fact, the use of the first person is far more common. This is because the researcher is central to the research project. Qualitative researchers can themselves be understood as research instruments, and thus eliminating the use of the first person in writing is in a sense eliminating information about the conduct of the researchers themselves.

But the question really extends beyond the issue of first-person or third-person. Qualitative researchers have choices about how and whether to foreground themselves in their writing, not just in terms of using the first person, but also in terms of whether to emphasize their own subjectivity and reflexivity, their impressions and ideas, and their role in the setting. In contrast, conventional quantitative research in the positivist tradition really tries to eliminate the author from the study—which indeed is exactly why typical quantitative research avoids the use of the first person. Keep in mind that emphasizing researchers’ roles and reflexivity and using the first person does not mean crafting articles that provide overwhelming detail about the author’s thoughts and practices. Readers do not need to hear, and should not be told, which database you used to search for journal articles, how many hours you spent transcribing, or whether the research process was stressful—save these things for the memos you write to yourself. Rather, readers need to hear how you interacted with research participants, how your standpoint may have shaped the findings, and what analytical procedures you carried out.

Making Data Come Alive

One of the most important parts of writing about qualitative research is presenting the data in a way that makes its richness and value accessible to readers. As the discussion of analysis in the prior chapter suggests, there are a variety of ways to do this. Researchers may select key quotes or images to illustrate points, write up specific case studies that exemplify their argument, or develop vignettes (little stories) that illustrate ideas and themes, all drawing directly on the research data. Researchers can also write more lengthy summaries, narratives, and thick descriptions.

Nearly all qualitative work includes quotes from research participants or documents to some extent, though ethnographic work may focus more on thick description than on relaying participants’ own words. When quotes are presented, they must be explained and interpreted—they cannot stand on their own. This is one of the ways in which qualitative research can be distinguished from journalism. Journalism presents what happened, but social science needs to present the “why,” and the why is best explained by the researcher.

So how do authors go about integrating quotes into their written work? Julie Posselt (2017), a sociologist who studies graduate education, provides a set of instructions. First of all, authors need to remain focused on the core questions of their research, and avoid getting distracted by quotes that are interesting or attention-grabbing but not so relevant to the research question. Selecting the right quotes, those that illustrate the ideas and arguments of the paper, is an important part of the writing process. Second, not all quotes should be the same length (just like not all sentences or paragraphs in a paper should be the same length). Include some quotes that are just phrases, others that are a sentence or so, and others that are longer. We call longer quotes, generally those more than about three lines long, block quotes , and they are typically indented on both sides to set them off from the surrounding text. For all quotes, be sure to summarize what the quote should be telling or showing the reader, connect this quote to other quotes that are similar or different, and provide transitions in the discussion to move from quote to quote and from topic to topic. Especially for longer quotes, it is helpful to do some of this writing before the quote to preview what is coming and other writing after the quote to make clear what readers should have come to understand. Remember, it is always the author’s job to interpret the data. Presenting excerpts of the data, like quotes, in a form the reader can access does not minimize the importance of this job. Be sure that you are explaining the meaning of the data you present.

A few more notes about writing with quotes: avoid patchwriting, whether in your literature review or the section of your paper in which quotes from respondents are presented. Patchwriting is a writing practice wherein the author lightly paraphrases original texts but stays so close to those texts that there is little the author has added. Sometimes, this even takes the form of presenting a series of quotes, properly documented, with nothing much in the way of text generated by the author. A patchwriting approach does not build the scholarly conversation forward, as it does not represent any kind of new contribution on the part of the author. It is of course fine to paraphrase quotes, as long as the meaning is not changed. But if you use direct quotes, do not edit the text of the quotes unless how you edit them does not change the meaning and you have made clear through the use of ellipses (…) and brackets ([])what kinds of edits have been made. For example, consider this exchange from Matthew Desmond’s (2012:1317) research on evictions:

The thing was, I wasn’t never gonna let Crystal come and stay with me from the get go. I just told her that to throw her off. And she wasn’t fittin’ to come stay with me with no money…No. Nope. You might as well stay in that shelter.

A paraphrase of this exchange might read “She said that she was going to let Crystal stay with her if Crystal did not have any money.” Paraphrases like that are fine. What is not fine is rewording the statement but treating it like a quote, for instance writing:

The thing was, I was not going to let Crystal come and stay with me from beginning. I just told her that to throw her off. And it was not proper for her to come stay with me without any money…No. Nope. You might as well stay in that shelter.

But as you can see, the change in language and style removes some of the distinct meaning of the original quote. Instead, writers should leave as much of the original language as possible. If some text in the middle of the quote needs to be removed, as in this example, ellipses are used to show that this has occurred. And if a word needs to be added to clarify, it is placed in square brackets to show that it was not part of the original quote.

Data can also be presented through the use of data displays like tables, charts, graphs, diagrams, and infographics created for publication or presentation, as well as through the use of visual material collected during the research process. Note that if visuals are used, the author must have the legal right to use them. Photographs or diagrams created by the author themselves—or by research participants who have signed consent forms for their work to be used, are fine. But photographs, and sometimes even excerpts from archival documents, may be owned by others from whom researchers must get permission in order to use them.

A large percentage of qualitative research does not include any data displays or visualizations. Therefore, researchers should carefully consider whether the use of data displays will help the reader understand the data. One of the most common types of data displays used by qualitative researchers are simple tables. These might include tables summarizing key data about cases included in the study; tables laying out the characteristics of different taxonomic elements or types developed as part of the analysis; tables counting the incidence of various elements; and 2×2 tables (two columns and two rows) illuminating a theory. Basic network or process diagrams are also commonly included. If data displays are used, it is essential that researchers include context and analysis alongside data displays rather than letting them stand by themselves, and it is preferable to continue to present excerpts and examples from the data rather than just relying on summaries in the tables.

If you will be using graphs, infographics, or other data visualizations, it is important that you attend to making them useful and accurate (Bergin 2018). Think about the viewer or user as your audience and ensure the data visualizations will be comprehensible. You may need to include more detail or labels than you might think. Ensure that data visualizations are laid out and labeled clearly and that you make visual choices that enhance viewers’ ability to understand the points you intend to communicate using the visual in question. Finally, given the ease with which it is possible to design visuals that are deceptive or misleading, it is essential to make ethical and responsible choices in the construction of visualization so that viewers will interpret them in accurate ways.

The Genre of Research Writing

As discussed above, the style and format in which results are presented depends on the audience they are intended for. These differences in styles and format are part of the genre of writing. Genre is a term referring to the rules of a specific form of creative or productive work. Thus, the academic journal article—and student papers based on this form—is one genre. A report or policy paper is another. The discussion below will focus on the academic journal article, but note that reports and policy papers follow somewhat different formats. They might begin with an executive summary of one or a few pages, include minimal background, focus on key findings, and conclude with policy implications, shifting methods and details about the data to an appendix. But both academic journal articles and policy papers share some things in common, for instance the necessity for clear writing, a well-organized structure, and the use of headings.

So what factors make up the genre of the academic journal article in sociology? While there is some flexibility, particularly for ethnographic work, academic journal articles tend to follow a fairly standard format. They begin with a “title page” that includes the article title (often witty and involving scholarly inside jokes, but more importantly clearly describing the content of the article); the authors’ names and institutional affiliations, an abstract , and sometimes keywords designed to help others find the article in databases. An abstract is a short summary of the article that appears both at the very beginning of the article and in search databases. Abstracts are designed to aid readers by giving them the opportunity to learn enough about an article that they can determine whether it is worth their time to read the complete text. They are written about the article, and thus not in the first person, and clearly summarize the research question, methodological approach, main findings, and often the implications of the research.

After the abstract comes an “introduction” of a page or two that details the research question, why it matters, and what approach the paper will take. This is followed by a literature review of about a quarter to a third the length of the entire paper. The literature review is often divided, with headings, into topical subsections, and is designed to provide a clear, thorough overview of the prior research literature on which a paper has built—including prior literature the new paper contradicts. At the end of the literature review it should be made clear what researchers know about the research topic and question, what they do not know, and what this new paper aims to do to address what is not known.

The next major section of the paper is the section that describes research design, data collection, and data analysis, often referred to as “research methods” or “methodology.” This section is an essential part of any written or oral presentation of your research. Here, you tell your readers or listeners “how you collected and interpreted your data” (Taylor, Bogdan, and DeVault 2016:215). Taylor, Bogdan, and DeVault suggest that the discussion of your research methods include the following:

• The particular approach to data collection used in the study;
• Any theoretical perspective(s) that shaped your data collection and analytical approach;
• When the study occurred, over how long, and where (concealing identifiable details as needed);
• A description of the setting and participants, including sampling and selection criteria (if an interview-based study, the number of participants should be clearly stated);
• The researcher’s perspective in carrying out the study, including relevant elements of their identity and standpoint, as well as their role (if any) in research settings; and
• The approach to analyzing the data.

After the methods section comes a section, variously titled but often called “data,” that takes readers through the analysis. This section is where the thick description narrative; the quotes, broken up by theme or topic, with their interpretation; the discussions of case studies; most data displays (other than perhaps those outlining a theoretical model or summarizing descriptive data about cases); and other similar material appears. The idea of the data section is to give readers the ability to see the data for themselves and to understand how this data supports the ultimate conclusions. Note that all tables and figures included in formal publications should be titled and numbered.

At the end of the paper come one or two summary sections, often called “discussion” and/or “conclusion.” If there is a separate discussion section, it will focus on exploring the overall themes and findings of the paper. The conclusion clearly and succinctly summarizes the findings and conclusions of the paper, the limitations of the research and analysis, any suggestions for future research building on the paper or addressing these limitations, and implications, be they for scholarship and theory or policy and practice.

After the end of the textual material in the paper comes the bibliography, typically called “works cited” or “references.” The references should appear in a consistent citation style—in sociology, we often use the American Sociological Association format (American Sociological Association 2019), but other formats may be used depending on where the piece will eventually be published. Care should be taken to ensure that in-text citations also reflect the chosen citation style. In some papers, there may be an appendix containing supplemental information such as a list of interview questions or an additional data visualization.

Note that when researchers give presentations to scholarly audiences, the presentations typically follow a format similar to that of scholarly papers, though given time limitations they are compressed. Abstracts and works cited are often not part of the presentation, though in-text citations are still used. The literature review presented will be shortened to only focus on the most important aspects of the prior literature, and only key examples from the discussion of data will be included. For long or complex papers, sometimes only one of several findings is the focus of the presentation. Of course, presentations for other audiences may be constructed differently, with greater attention to interesting elements of the data and findings as well as implications and less to the literature review and methods.

Concluding Your Work

After you have written a complete draft of the paper, be sure you take the time to revise and edit your work. There are several important strategies for revision. First, put your work away for a little while. Even waiting a day to revise is better than nothing, but it is best, if possible, to take much more time away from the text. This helps you forget what your writing looks like and makes it easier to find errors, mistakes, and omissions. Second, show your work to others. Ask them to read your work and critique it, pointing out places where the argument is weak, where you may have overlooked alternative explanations, where the writing could be improved, and what else you need to work on. Finally, read your work out loud to yourself (or, if you really need an audience, try reading to some stuffed animals). Reading out loud helps you catch wrong words, tricky sentences, and many other issues. But as important as revision is, try to avoid perfectionism in writing (Warren and Karner 2015). Writing can always be improved, no matter how much time you spend on it. Those improvements, however, have diminishing returns, and at some point the writing process needs to conclude so the writing can be shared with the world.

Of course, the main goal of writing up the results of a research project is to share with others. Thus, researchers should be considering how they intend to disseminate their results. What conferences might be appropriate? Where can the paper be submitted? Note that if you are an undergraduate student, there are a wide variety of journals that accept and publish research conducted by undergraduates. Some publish across disciplines, while others are specific to disciplines. Other work, such as reports, may be best disseminated by publication online on relevant organizational websites.

After a project is completed, be sure to take some time to organize your research materials and archive them for longer-term storage. Some Institutional Review Board (IRB) protocols require that original data, such as interview recordings, transcripts, and field notes, be preserved for a specific number of years in a protected (locked for paper or password-protected for digital) form and then destroyed, so be sure that your plans adhere to the IRB requirements. Be sure you keep any materials that might be relevant for future related research or for answering questions people may ask later about your project.

And then what? Well, then it is time to move on to your next research project. Research is a long-term endeavor, not a one-time-only activity. We build our skills and our expertise as we continue to pursue research. So keep at it.

• Find a short article that uses qualitative methods. The sociological magazine Contexts is a good place to find such pieces. Write an abstract of the article.
• Choose a sociological journal article on a topic you are interested in that uses some form of qualitative methods and is at least 20 pages long. Rewrite the article as a five-page research summary accessible to non-scholarly audiences.
• Choose a concept or idea you have learned in this course and write an explanation of it using the Up-Goer Five Text Editor ( https://www.splasho.com/upgoer5/ ), a website that restricts your writing to the 1,000 most common English words. What was this experience like? What did it teach you about communicating with people who have a more limited English-language vocabulary—and what did it teach you about the utility of having access to complex academic language?
• Select five or more sociological journal articles that all use the same basic type of qualitative methods (interviewing, ethnography, documents, or visual sociology). Using what you have learned about coding, code the methods sections of each article, and use your coding to figure out what is common in how such articles discuss their research design, data collection, and analysis methods.
• Return to an exercise you completed earlier in this course and revise your work. What did you change? How did revising impact the final product?
• Find a quote from the transcript of an interview, a social media post, or elsewhere that has not yet been interpreted or explained. Write a paragraph that includes the quote along with an explanation of its sociological meaning or significance.

The style or personality of a piece of writing, including such elements as tone, word choice, syntax, and rhythm.

A quotation, usually one of some length, which is set off from the main text by being indented on both sides rather than being placed in quotation marks.

A classification of written or artistic work based on form, content, and style.

A short summary of a text written from the perspective of a reader rather than from the perspective of an author.

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Presenting your qualitative analysis findings: tables to include in chapter 4.

The earliest stages of developing a doctoral dissertation—most specifically the topic development  and literature review  stages—require that you immerse yourself in a ton of existing research related to your potential topic. If you have begun writing your dissertation proposal, you have undoubtedly reviewed countless results and findings sections of studies in order to help gain an understanding of what is currently known about your topic. 

In this process, we’re guessing that you observed a distinct pattern: Results sections are full of tables. Indeed, the results chapter for your own dissertation will need to be similarly packed with tables. So, if you’re preparing to write up the results of your statistical analysis or qualitative analysis, it will probably help to review your APA editing  manual to brush up on your table formatting skills. But, aside from formatting, how should you develop the tables in your results chapter?

In quantitative studies, tables are a handy way of presenting the variety of statistical analysis results in a form that readers can easily process. You’ve probably noticed that quantitative studies present descriptive results like mean, mode, range, standard deviation, etc., as well the inferential results that indicate whether significant relationships or differences were found through the statistical analysis . These are pretty standard tables that you probably learned about in your pre-dissertation statistics courses.

But, what if you are conducting qualitative analysis? What tables are appropriate for this type of study? This is a question we hear often from our dissertation assistance  clients, and with good reason. University guidelines for results chapters often contain vague instructions that guide you to include “appropriate tables” without specifying what exactly those are. To help clarify on this point, we asked our qualitative analysis experts to share their recommendations for tables to include in your Chapter 4.

Demographics Tables

As with studies using quantitative methods , presenting an overview of your sample demographics is useful in studies that use qualitative research methods. The standard demographics table in a quantitative study provides aggregate information for what are often large samples. In other words, such tables present totals and percentages for demographic categories within the sample that are relevant to the study (e.g., age, gender, job title). 

If conducting qualitative research  for your dissertation, however, you will use a smaller sample and obtain richer data from each participant than in quantitative studies. To enhance thick description—a dimension of trustworthiness—it will help to present sample demographics in a table that includes information on each participant. Remember that ethical standards of research require that all participant information be deidentified, so use participant identification numbers or pseudonyms for each participant, and do not present any personal information that would allow others to identify the participant (Blignault & Ritchie, 2009). Table 1 provides participant demographics for a hypothetical qualitative research study exploring the perspectives of persons who were formerly homeless regarding their experiences of transitioning into stable housing and obtaining employment.

Participant Demographics

Tables to Illustrate Initial Codes

Most of our dissertation consulting clients who are conducting qualitative research choose a form of thematic analysis . Qualitative analysis to identify themes in the data typically involves a progression from (a) identifying surface-level codes to (b) developing themes by combining codes based on shared similarities. As this process is inherently subjective, it is important that readers be able to evaluate the correspondence between the data and your findings (Anfara et al., 2002). This supports confirmability, another dimension of trustworthiness .

A great way to illustrate the trustworthiness of your qualitative analysis is to create a table that displays quotes from the data that exemplify each of your initial codes. Providing a sample quote for each of your codes can help the reader to assess whether your coding was faithful to the meanings in the data, and it can also help to create clarity about each code’s meaning and bring the voices of your participants into your work (Blignault & Ritchie, 2009).

Table 2 is an example of how you might present information regarding initial codes. Depending on your preference or your dissertation committee’s preference, you might also present percentages of the sample that expressed each code. Another common piece of information to include is which actual participants expressed each code. Note that if your qualitative analysis yields a high volume of codes, it may be appropriate to present the table as an appendix.

Initial Codes

Tables to Present the Groups of Codes That Form Each Theme

As noted previously, most of our dissertation assistance clients use a thematic analysis approach, which involves multiple phases of qualitative analysis  that eventually result in themes that answer the dissertation’s research questions. After initial coding is completed, the analysis process involves (a) examining what different codes have in common and then (b) grouping similar codes together in ways that are meaningful given your research questions. In other words, the common threads that you identify across multiple codes become the theme that holds them all together—and that theme answers one of your research questions.

As with initial coding, grouping codes together into themes involves your own subjective interpretations, even when aided by qualitative analysis software such as NVivo  or MAXQDA. In fact, our dissertation assistance clients are often surprised to learn that qualitative analysis software does not complete the analysis in the same ways that statistical analysis software such as SPSS does. While statistical analysis software completes the computations for you, qualitative analysis software does not have such analysis capabilities. Software such as NVivo provides a set of organizational tools that make the qualitative analysis far more convenient, but the analysis itself is still a very human process (Burnard et al., 2008).

Because of the subjective nature of qualitative analysis, it is important to show the underlying logic behind your thematic analysis in tables—such tables help readers to assess the trustworthiness of your analysis. Table 3 provides an example of how to present the codes that were grouped together to create themes, and you can modify the specifics of the table based on your preferences or your dissertation committee’s requirements. For example, this type of table might be presented to illustrate the codes associated with themes that answer each research question. 

Grouping of Initial Codes to Form Themes

Tables to Illustrate the Themes That Answer Each Research Question

Creating alignment throughout your dissertation is an important objective, and to maintain alignment in your results chapter, the themes you present must clearly answer your research questions. Conducting qualitative analysis is an in-depth process of immersion in the data, and many of our dissertation consulting  clients have shared that it’s easy to lose your direction during the process. So, it is important to stay focused on your research questions during the qualitative analysis and also to show the reader exactly which themes—and subthemes, as applicable—answered each of the research questions.

Below, Table 4 provides an example of how to display the thematic findings of your study in table form. Depending on your dissertation committee’s preference or your own, you might present all research questions and all themes and subthemes in a single table. Or, you might provide separate tables to introduce the themes for each research question as you progress through your presentation of the findings in the chapter.

Emergent Themes and Research Questions

Bonus Tip! Figures to Spice Up Your Results

Although dissertation committees most often wish to see tables such as the above in qualitative results chapters, some also like to see figures that illustrate the data. Qualitative software packages such as NVivo offer many options for visualizing your data, such as mind maps, concept maps, charts, and cluster diagrams. A common choice for this type of figure among our dissertation assistance clients is a tree diagram, which shows the connections between specified words and the words or phrases that participants shared most often in the same context. Another common choice of figure is the word cloud, as depicted in Figure 1. The word cloud simply reflects frequencies of words in the data, which may provide an indication of the importance of related concepts for the participants.

As you move forward with your qualitative analysis and development of your results chapter, we hope that this brief overview of useful tables and figures helps you to decide on an ideal presentation to showcase the trustworthiness your findings. Completing a rigorous qualitative analysis for your dissertation requires many hours of careful interpretation of your data, and your end product should be a rich and detailed results presentation that you can be proud of. Reach out if we can help  in any way, as our dissertation coaches would be thrilled to assist as you move through this exciting stage of your dissertation journey!

Anfara Jr., V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage: Making the research process more public.  Educational Researcher ,  31 (7), 28-38. https://doi.org/10.3102/0013189X031007028

Blignault, I., & Ritchie, J. (2009). Revealing the wood and the trees: Reporting qualitative research.  Health Promotion Journal of Australia ,  20 (2), 140-145. https://doi.org/10.1071/HE09140

Burnard, P., Gill, P., Stewart, K., Treasure, E., & Chadwick, B. (2008). Analysing and presenting qualitative data.  British Dental Journal ,  204 (8), 429-432. https://doi.org/10.1038/sj.bdj.2008.292

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Cochrane Training

Chapter 14: completing ‘summary of findings’ tables and grading the certainty of the evidence.

Holger J Schünemann, Julian PT Higgins, Gunn E Vist, Paul Glasziou, Elie A Akl, Nicole Skoetz, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group (formerly Applicability and Recommendations Methods Group) and the Cochrane Statistical Methods Group

Key Points:

• A ‘Summary of findings’ table for a given comparison of interventions provides key information concerning the magnitudes of relative and absolute effects of the interventions examined, the amount of available evidence and the certainty (or quality) of available evidence.
• ‘Summary of findings’ tables include a row for each important outcome (up to a maximum of seven). Accepted formats of ‘Summary of findings’ tables and interactive ‘Summary of findings’ tables can be produced using GRADE’s software GRADEpro GDT.
• Cochrane has adopted the GRADE approach (Grading of Recommendations Assessment, Development and Evaluation) for assessing certainty (or quality) of a body of evidence.
• The GRADE approach specifies four levels of the certainty for a body of evidence for a given outcome: high, moderate, low and very low.
• GRADE assessments of certainty are determined through consideration of five domains: risk of bias, inconsistency, indirectness, imprecision and publication bias. For evidence from non-randomized studies and rarely randomized studies, assessments can then be upgraded through consideration of three further domains.

Cite this chapter as: Schünemann HJ, Higgins JPT, Vist GE, Glasziou P, Akl EA, Skoetz N, Guyatt GH. Chapter 14: Completing ‘Summary of findings’ tables and grading the certainty of the evidence. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook .

14.1 ‘Summary of findings’ tables

14.1.1 introduction to ‘summary of findings’ tables.

‘Summary of findings’ tables present the main findings of a review in a transparent, structured and simple tabular format. In particular, they provide key information concerning the certainty or quality of evidence (i.e. the confidence or certainty in the range of an effect estimate or an association), the magnitude of effect of the interventions examined, and the sum of available data on the main outcomes. Cochrane Reviews should incorporate ‘Summary of findings’ tables during planning and publication, and should have at least one key ‘Summary of findings’ table representing the most important comparisons. Some reviews may include more than one ‘Summary of findings’ table, for example if the review addresses more than one major comparison, or includes substantially different populations that require separate tables (e.g. because the effects differ or it is important to show results separately). In the Cochrane Database of Systematic Reviews (CDSR),  all ‘Summary of findings’ tables for a review appear at the beginning, before the Background section.

14.1.2 Selecting outcomes for ‘Summary of findings’ tables

Planning for the ‘Summary of findings’ table starts early in the systematic review, with the selection of the outcomes to be included in: (i) the review; and (ii) the ‘Summary of findings’ table. This is a crucial step, and one that review authors need to address carefully.

To ensure production of optimally useful information, Cochrane Reviews begin by developing a review question and by listing all main outcomes that are important to patients and other decision makers (see Chapter 2 and Chapter 3 ). The GRADE approach to assessing the certainty of the evidence (see Section 14.2 ) defines and operationalizes a rating process that helps separate outcomes into those that are critical, important or not important for decision making. Consultation and feedback on the review protocol, including from consumers and other decision makers, can enhance this process.

Critical outcomes are likely to include clearly important endpoints; typical examples include mortality and major morbidity (such as strokes and myocardial infarction). However, they may also represent frequent minor and rare major side effects, symptoms, quality of life, burdens associated with treatment, and resource issues (costs). Burdens represent the impact of healthcare workload on patient function and well-being, and include the demands of adhering to an intervention that patients or caregivers (e.g. family) may dislike, such as having to undergo more frequent tests, or the restrictions on lifestyle that certain interventions require (Spencer-Bonilla et al 2017).

Frequently, when formulating questions that include all patient-important outcomes for decision making, review authors will confront reports of studies that have not included all these outcomes. This is particularly true for adverse outcomes. For instance, randomized trials might contribute evidence on intended effects, and on frequent, relatively minor side effects, but not report on rare adverse outcomes such as suicide attempts. Chapter 19 discusses strategies for addressing adverse effects. To obtain data for all important outcomes it may be necessary to examine the results of non-randomized studies (see Chapter 24 ). Cochrane, in collaboration with others, has developed guidance for review authors to support their decision about when to look for and include non-randomized studies (Schünemann et al 2013).

If a review includes only randomized trials, these trials may not address all important outcomes and it may therefore not be possible to address these outcomes within the constraints of the review. Review authors should acknowledge these limitations and make them transparent to readers. Review authors are encouraged to include non-randomized studies to examine rare or long-term adverse effects that may not adequately be studied in randomized trials. This raises the possibility that harm outcomes may come from studies in which participants differ from those in studies used in the analysis of benefit. Review authors will then need to consider how much such differences are likely to impact on the findings, and this will influence the certainty of evidence because of concerns about indirectness related to the population (see Section 14.2.2 ).

Non-randomized studies can provide important information not only when randomized trials do not report on an outcome or randomized trials suffer from indirectness, but also when the evidence from randomized trials is rated as very low and non-randomized studies provide evidence of higher certainty. Further discussion of these issues appears also in Chapter 24 .

14.1.3 General template for ‘Summary of findings’ tables

Several alternative standard versions of ‘Summary of findings’ tables have been developed to ensure consistency and ease of use across reviews, inclusion of the most important information needed by decision makers, and optimal presentation (see examples at Figures 14.1.a and 14.1.b ). These formats are supported by research that focused on improved understanding of the information they intend to convey (Carrasco-Labra et al 2016, Langendam et al 2016, Santesso et al 2016). They are available through GRADE’s official software package developed to support the GRADE approach: GRADEpro GDT (www.gradepro.org).

Standard Cochrane ‘Summary of findings’ tables include the following elements using one of the accepted formats. Further guidance on each of these is provided in Section 14.1.6 .

• A brief description of the population and setting addressed by the available evidence (which may be slightly different to or narrower than those defined by the review question).
• A brief description of the comparison addressed in the ‘Summary of findings’ table, including both the experimental and comparison interventions.
• A list of the most critical and/or important health outcomes, both desirable and undesirable, limited to seven or fewer outcomes.
• A measure of the typical burden of each outcomes (e.g. illustrative risk, or illustrative mean, on comparator intervention).
• The absolute and relative magnitude of effect measured for each (if both are appropriate).
• The numbers of participants and studies contributing to the analysis of each outcomes.
• A GRADE assessment of the overall certainty of the body of evidence for each outcome (which may vary by outcome).
• Space for comments.
• Explanations (formerly known as footnotes).

Ideally, ‘Summary of findings’ tables are supported by more detailed tables (known as ‘evidence profiles’) to which the review may be linked, which provide more detailed explanations. Evidence profiles include the same important health outcomes, and provide greater detail than ‘Summary of findings’ tables of both of the individual considerations feeding into the grading of certainty and of the results of the studies (Guyatt et al 2011a). They ensure that a structured approach is used to rating the certainty of evidence. Although they are rarely published in Cochrane Reviews, evidence profiles are often used, for example, by guideline developers in considering the certainty of the evidence to support guideline recommendations. Review authors will find it easier to develop the ‘Summary of findings’ table by completing the rating of the certainty of evidence in the evidence profile first in GRADEpro GDT. They can then automatically convert this to one of the ‘Summary of findings’ formats in GRADEpro GDT, including an interactive ‘Summary of findings’ for publication.

As a measure of the magnitude of effect for dichotomous outcomes, the ‘Summary of findings’ table should provide a relative measure of effect (e.g. risk ratio, odds ratio, hazard) and measures of absolute risk. For other types of data, an absolute measure alone (such as a difference in means for continuous data) might be sufficient. It is important that the magnitude of effect is presented in a meaningful way, which may require some transformation of the result of a meta-analysis (see also Chapter 15, Section 15.4 and Section 15.5 ). Reviews with more than one main comparison should include a separate ‘Summary of findings’ table for each comparison.

Figure 14.1.a provides an example of a ‘Summary of findings’ table. Figure 15.1.b  provides an alternative format that may further facilitate users’ understanding and interpretation of the review’s findings. Evidence evaluating different formats suggests that the ‘Summary of findings’ table should include a risk difference as a measure of the absolute effect and authors should preferably use a format that includes a risk difference .

A detailed description of the contents of a ‘Summary of findings’ table appears in Section 14.1.6 .

Figure 14.1.a Example of a ‘Summary of findings’ table

Summary of findings (for interactive version click here )

a All the stockings in the nine studies included in this review were below-knee compression stockings. In four studies the compression strength was 20 mmHg to 30 mmHg at the ankle. It was 10 mmHg to 20 mmHg in the other four studies. Stockings come in different sizes. If a stocking is too tight around the knee it can prevent essential venous return causing the blood to pool around the knee. Compression stockings should be fitted properly. A stocking that is too tight could cut into the skin on a long flight and potentially cause ulceration and increased risk of DVT. Some stockings can be slightly thicker than normal leg covering and can be potentially restrictive with tight foot wear. It is a good idea to wear stockings around the house prior to travel to ensure a good, comfortable fit. Participants put their stockings on two to three hours before the flight in most of the studies. The availability and cost of stockings can vary.

b Two studies recruited high risk participants defined as those with previous episodes of DVT, coagulation disorders, severe obesity, limited mobility due to bone or joint problems, neoplastic disease within the previous two years, large varicose veins or, in one of the studies, participants taller than 190 cm and heavier than 90 kg. The incidence for the seven studies that excluded high risk participants was 1.45% and the incidence for the two studies that recruited high-risk participants (with at least one risk factor) was 2.43%. We have used 10 and 30 per 1000 to express different risk strata, respectively.

c The confidence interval crosses no difference and does not rule out a small increase.

d The measurement of oedema was not validated (indirectness of the outcome) or blinded to the intervention (risk of bias).

e If there are very few or no events and the number of participants is large, judgement about the certainty of evidence (particularly judgements about imprecision) may be based on the absolute effect. Here the certainty rating may be considered ‘high’ if the outcome was appropriately assessed and the event, in fact, did not occur in 2821 studied participants.

f None of the other studies reported adverse effects, apart from four cases of superficial vein thrombosis in varicose veins in the knee region that were compressed by the upper edge of the stocking in one study.

Figure 14.1.b Example of alternative ‘Summary of findings’ table

14.1.4 Producing ‘Summary of findings’ tables

The GRADE Working Group’s software, GRADEpro GDT ( www.gradepro.org ), including GRADE’s interactive handbook, is available to assist review authors in the preparation of ‘Summary of findings’ tables. GRADEpro can use data on the comparator group risk and the effect estimate (entered by the review authors or imported from files generated in RevMan) to produce the relative effects and absolute risks associated with experimental interventions. In addition, it leads the user through the process of a GRADE assessment, and produces a table that can be used as a standalone table in a review (including by direct import into software such as RevMan or integration with RevMan Web), or an interactive ‘Summary of findings’ table (see help resources in GRADEpro).

14.1.5 Statistical considerations in ‘Summary of findings’ tables

14.1.5.1 dichotomous outcomes.

‘Summary of findings’ tables should include both absolute and relative measures of effect for dichotomous outcomes. Risk ratios, odds ratios and risk differences are different ways of comparing two groups with dichotomous outcome data (see Chapter 6, Section 6.4.1 ). Furthermore, there are two distinct risk ratios, depending on which event (e.g. ‘yes’ or ‘no’) is the focus of the analysis (see Chapter 6, Section 6.4.1.5 ). In the presence of a non-zero intervention effect, any variation across studies in the comparator group risks (i.e. variation in the risk of the event occurring without the intervention of interest, for example in different populations) makes it impossible for more than one of these measures to be truly the same in every study.

It has long been assumed in epidemiology that relative measures of effect are more consistent than absolute measures of effect from one scenario to another. There is empirical evidence to support this assumption (Engels et al 2000, Deeks and Altman 2001, Furukawa et al 2002). For this reason, meta-analyses should generally use either a risk ratio or an odds ratio as a measure of effect (see Chapter 10, Section 10.4.3 ). Correspondingly, a single estimate of relative effect is likely to be a more appropriate summary than a single estimate of absolute effect. If a relative effect is indeed consistent across studies, then different comparator group risks will have different implications for absolute benefit. For instance, if the risk ratio is consistently 0.75, then the experimental intervention would reduce a comparator group risk of 80% to 60% in the intervention group (an absolute risk reduction of 20 percentage points), but would also reduce a comparator group risk of 20% to 15% in the intervention group (an absolute risk reduction of 5 percentage points).

‘Summary of findings’ tables are built around the assumption of a consistent relative effect. It is therefore important to consider the implications of this effect for different comparator group risks (these can be derived or estimated from a number of sources, see Section 14.1.6.3 ), which may require an assessment of the certainty of evidence for prognostic evidence (Spencer et al 2012, Iorio et al 2015). For any comparator group risk, it is possible to estimate a corresponding intervention group risk (i.e. the absolute risk with the intervention) from the meta-analytic risk ratio or odds ratio. Note that the numbers provided in the ‘Corresponding risk’ column are specific to the ‘risks’ in the adjacent column.

For the meta-analytic risk ratio (RR) and assumed comparator risk (ACR) the corresponding intervention risk is obtained as:

As an example, in Figure 14.1.a , the meta-analytic risk ratio for symptomless deep vein thrombosis (DVT) is RR = 0.10 (95% CI 0.04 to 0.26). Assuming a comparator risk of ACR = 10 per 1000 = 0.01, we obtain:

For the meta-analytic odds ratio (OR) and assumed comparator risk, ACR, the corresponding intervention risk is obtained as:

Upper and lower confidence limits for the corresponding intervention risk are obtained by replacing RR or OR by their upper and lower confidence limits, respectively (e.g. replacing 0.10 with 0.04, then with 0.26, in the example). Such confidence intervals do not incorporate uncertainty in the assumed comparator risks.

When dealing with risk ratios, it is critical that the same definition of ‘event’ is used as was used for the meta-analysis. For example, if the meta-analysis focused on ‘death’ (as opposed to survival) as the event, then corresponding risks in the ‘Summary of findings’ table must also refer to ‘death’.

In (rare) circumstances in which there is clear rationale to assume a consistent risk difference in the meta-analysis, in principle it is possible to present this for relevant ‘assumed risks’ and their corresponding risks, and to present the corresponding (different) relative effects for each assumed risk.

The risk difference expresses the difference between the ACR and the corresponding intervention risk (or the difference between the experimental and the comparator intervention).

For the meta-analytic risk ratio (RR) and assumed comparator risk (ACR) the corresponding risk difference is obtained as (note that risks can also be expressed using percentage or percentage points):

As an example, in Figure 14.1.b the meta-analytic risk ratio is 0.41 (95% CI 0.29 to 0.55) for diarrhoea in children less than 5 years of age. Assuming a comparator group risk of 22.3% we obtain:

For the meta-analytic odds ratio (OR) and assumed comparator risk (ACR) the absolute risk difference is obtained as (percentage points):

Upper and lower confidence limits for the absolute risk difference are obtained by re-running the calculation above while replacing RR or OR by their upper and lower confidence limits, respectively (e.g. replacing 0.41 with 0.28, then with 0.55, in the example). Such confidence intervals do not incorporate uncertainty in the assumed comparator risks.

14.1.5.2 Time-to-event outcomes

Time-to-event outcomes measure whether and when a particular event (e.g. death) occurs (van Dalen et al 2007). The impact of the experimental intervention relative to the comparison group on time-to-event outcomes is usually measured using a hazard ratio (HR) (see Chapter 6, Section 6.8.1 ).

A hazard ratio expresses a relative effect estimate. It may be used in various ways to obtain absolute risks and other interpretable quantities for a specific population. Here we describe how to re-express hazard ratios in terms of: (i) absolute risk of event-free survival within a particular period of time; (ii) absolute risk of an event within a particular period of time; and (iii) median time to the event. All methods are built on an assumption of consistent relative effects (i.e. that the hazard ratio does not vary over time).

(i) Absolute risk of event-free survival within a particular period of time Event-free survival (e.g. overall survival) is commonly reported by individual studies. To obtain absolute effects for time-to-event outcomes measured as event-free survival, the summary HR can be used in conjunction with an assumed proportion of patients who are event-free in the comparator group (Tierney et al 2007). This proportion of patients will be specific to a period of time of observation. However, it is not strictly necessary to specify this period of time. For instance, a proportion of 50% of event-free patients might apply to patients with a high event rate observed over 1 year, or to patients with a low event rate observed over 2 years.

As an example, suppose the meta-analytic hazard ratio is 0.42 (95% CI 0.25 to 0.72). Assuming a comparator group risk of event-free survival (e.g. for overall survival people being alive) at 2 years of ACR = 900 per 1000 = 0.9 we obtain:

so that that 956 per 1000 people will be alive with the experimental intervention at 2 years. The derivation of the risk should be explained in a comment or footnote.

(ii) Absolute risk of an event within a particular period of time To obtain this absolute effect, again the summary HR can be used (Tierney et al 2007):

In the example, suppose we assume a comparator group risk of events (e.g. for mortality, people being dead) at 2 years of ACR = 100 per 1000 = 0.1. We obtain:

so that that 44 per 1000 people will be dead with the experimental intervention at 2 years.

(iii) Median time to the event Instead of absolute numbers, the time to the event in the intervention and comparison groups can be expressed as median survival time in months or years. To obtain median survival time the pooled HR can be applied to an assumed median survival time in the comparator group (Tierney et al 2007):

In the example, assuming a comparator group median survival time of 80 months, we obtain:

For all three of these options for re-expressing results of time-to-event analyses, upper and lower confidence limits for the corresponding intervention risk are obtained by replacing HR by its upper and lower confidence limits, respectively (e.g. replacing 0.42 with 0.25, then with 0.72, in the example). Again, as for dichotomous outcomes, such confidence intervals do not incorporate uncertainty in the assumed comparator group risks. This is of special concern for long-term survival with a low or moderate mortality rate and a corresponding high number of censored patients (i.e. a low number of patients under risk and a high censoring rate).

14.1.6 Detailed contents of a ‘Summary of findings’ table

14.1.6.1 table title and header.

The title of each ‘Summary of findings’ table should specify the healthcare question, framed in terms of the population and making it clear exactly what comparison of interventions are made. In Figure 14.1.a , the population is people taking long aeroplane flights, the intervention is compression stockings, and the control is no compression stockings.

The first rows of each ‘Summary of findings’ table should provide the following ‘header’ information:

Patients or population This further clarifies the population (and possibly the subpopulations) of interest and ideally the magnitude of risk of the most crucial adverse outcome at which an intervention is directed. For instance, people on a long-haul flight may be at different risks for DVT; those using selective serotonin reuptake inhibitors (SSRIs) might be at different risk for side effects; while those with atrial fibrillation may be at low (< 1%), moderate (1% to 4%) or high (> 4%) yearly risk of stroke.

Setting This should state any specific characteristics of the settings of the healthcare question that might limit the applicability of the summary of findings to other settings (e.g. primary care in Europe and North America).

Intervention The experimental intervention.

Comparison The comparator intervention (including no specific intervention).

14.1.6.2 Outcomes

The rows of a ‘Summary of findings’ table should include all desirable and undesirable health outcomes (listed in order of importance) that are essential for decision making, up to a maximum of seven outcomes. If there are more outcomes in the review, review authors will need to omit the less important outcomes from the table, and the decision selecting which outcomes are critical or important to the review should be made during protocol development (see Chapter 3 ). Review authors should provide time frames for the measurement of the outcomes (e.g. 90 days or 12 months) and the type of instrument scores (e.g. ranging from 0 to 100).

Note that review authors should include the pre-specified critical and important outcomes in the table whether data are available or not. However, they should be alert to the possibility that the importance of an outcome (e.g. a serious adverse effect) may only become known after the protocol was written or the analysis was carried out, and should take appropriate actions to include these in the ‘Summary of findings’ table.

The ‘Summary of findings’ table can include effects in subgroups of the population for different comparator risks and effect sizes separately. For instance, in Figure 14.1.b effects are presented for children younger and older than 5 years separately. Review authors may also opt to produce separate ‘Summary of findings’ tables for different populations.

Review authors should include serious adverse events, but it might be possible to combine minor adverse events as a single outcome, and describe this in an explanatory footnote (note that it is not appropriate to add events together unless they are independent, that is, a participant who has experienced one adverse event has an unaffected chance of experiencing the other adverse event).

Outcomes measured at multiple time points represent a particular problem. In general, to keep the table simple, review authors should present multiple time points only for outcomes critical to decision making, where either the result or the decision made are likely to vary over time. The remainder should be presented at a common time point where possible.

Review authors can present continuous outcome measures in the ‘Summary of findings’ table and should endeavour to make these interpretable to the target audience. This requires that the units are clear and readily interpretable, for example, days of pain, or frequency of headache, and the name and scale of any measurement tools used should be stated (e.g. a Visual Analogue Scale, ranging from 0 to 100). However, many measurement instruments are not readily interpretable by non-specialist clinicians or patients, for example, points on a Beck Depression Inventory or quality of life score. For these, a more interpretable presentation might involve converting a continuous to a dichotomous outcome, such as >50% improvement (see Chapter 15, Section 15.5 ).

14.1.6.3 Best estimate of risk with comparator intervention

Review authors should provide up to three typical risks for participants receiving the comparator intervention. For dichotomous outcomes, we recommend that these be presented in the form of the number of people experiencing the event per 100 or 1000 people (natural frequency) depending on the frequency of the outcome. For continuous outcomes, this would be stated as a mean or median value of the outcome measured.

Estimated or assumed comparator intervention risks could be based on assessments of typical risks in different patient groups derived from the review itself, individual representative studies in the review, or risks derived from a systematic review of prognosis studies or other sources of evidence which may in turn require an assessment of the certainty for the prognostic evidence (Spencer et al 2012, Iorio et al 2015). Ideally, risks would reflect groups that clinicians can easily identify on the basis of their presenting features.

An explanatory footnote should specify the source or rationale for each comparator group risk, including the time period to which it corresponds where appropriate. In Figure 14.1.a , clinicians can easily differentiate individuals with risk factors for deep venous thrombosis from those without. If there is known to be little variation in baseline risk then review authors may use the median comparator group risk across studies. If typical risks are not known, an option is to choose the risk from the included studies, providing the second highest for a high and the second lowest for a low risk population.

14.1.6.4 Risk with intervention

For dichotomous outcomes, review authors should provide a corresponding absolute risk for each comparator group risk, along with a confidence interval. This absolute risk with the (experimental) intervention will usually be derived from the meta-analysis result presented in the relative effect column (see Section 14.1.6.6 ). Formulae are provided in Section 14.1.5 . Review authors should present the absolute effect in the same format as the risks with comparator intervention (see Section 14.1.6.3 ), for example as the number of people experiencing the event per 1000 people.

For continuous outcomes, a difference in means or standardized difference in means should be presented with its confidence interval. These will typically be obtained directly from a meta-analysis. Explanatory text should be used to clarify the meaning, as in Figures 14.1.a and 14.1.b .

14.1.6.5 Risk difference

For dichotomous outcomes, the risk difference can be provided using one of the ‘Summary of findings’ table formats as an additional option (see Figure 14.1.b ). This risk difference expresses the difference between the experimental and comparator intervention and will usually be derived from the meta-analysis result presented in the relative effect column (see Section 14.1.6.6 ). Formulae are provided in Section 14.1.5 . Review authors should present the risk difference in the same format as assumed and corresponding risks with comparator intervention (see Section 14.1.6.3 ); for example, as the number of people experiencing the event per 1000 people or as percentage points if the assumed and corresponding risks are expressed in percentage.

For continuous outcomes, if the ‘Summary of findings’ table includes this option, the mean difference can be presented here and the ‘corresponding risk’ column left blank (see Figure 14.1.b ).

14.1.6.6 Relative effect (95% CI)

The relative effect will typically be a risk ratio or odds ratio (or occasionally a hazard ratio) with its accompanying 95% confidence interval, obtained from a meta-analysis performed on the basis of the same effect measure. Risk ratios and odds ratios are similar when the comparator intervention risks are low and effects are small, but may differ considerably when comparator group risks increase. The meta-analysis may involve an assumption of either fixed or random effects, depending on what the review authors consider appropriate, and implying that the relative effect is either an estimate of the effect of the intervention, or an estimate of the average effect of the intervention across studies, respectively.

14.1.6.7 Number of participants (studies)

This column should include the number of participants assessed in the included studies for each outcome and the corresponding number of studies that contributed these participants.

14.1.6.8 Certainty of the evidence (GRADE)

Review authors should comment on the certainty of the evidence (also known as quality of the body of evidence or confidence in the effect estimates). Review authors should use the specific evidence grading system developed by the GRADE Working Group (Atkins et al 2004, Guyatt et al 2008, Guyatt et al 2011a), which is described in detail in Section 14.2 . The GRADE approach categorizes the certainty in a body of evidence as ‘high’, ‘moderate’, ‘low’ or ‘very low’ by outcome. This is a result of judgement, but the judgement process operates within a transparent structure. As an example, the certainty would be ‘high’ if the summary were of several randomized trials with low risk of bias, but the rating of certainty becomes lower if there are concerns about risk of bias, inconsistency, indirectness, imprecision or publication bias. Judgements other than of ‘high’ certainty should be made transparent using explanatory footnotes or the ‘Comments’ column in the ‘Summary of findings’ table (see Section 14.1.6.10 ).

14.1.6.9 Comments

The aim of the ‘Comments’ field is to help interpret the information or data identified in the row. For example, this may be on the validity of the outcome measure or the presence of variables that are associated with the magnitude of effect. Important caveats about the results should be flagged here. Not all rows will need comments, and it is best to leave a blank if there is nothing warranting a comment.

14.1.6.10 Explanations

Detailed explanations should be included as footnotes to support the judgements in the ‘Summary of findings’ table, such as the overall GRADE assessment. The explanations should describe the rationale for important aspects of the content. Table 14.1.a lists guidance for useful explanations. Explanations should be concise, informative, relevant, easy to understand and accurate. If explanations cannot be sufficiently described in footnotes, review authors should provide further details of the issues in the Results and Discussion sections of the review.

Table 14.1.a Guidance for providing useful explanations in ‘Summary of findings’ (SoF) tables. Adapted from Santesso et al (2016)

14.2 Assessing the certainty or quality of a body of evidence

14.2.1 the grade approach.

The Grades of Recommendation, Assessment, Development and Evaluation Working Group (GRADE Working Group) has developed a system for grading the certainty of evidence (Schünemann et al 2003, Atkins et al 2004, Schünemann et al 2006, Guyatt et al 2008, Guyatt et al 2011a). Over 100 organizations including the World Health Organization (WHO), the American College of Physicians, the American Society of Hematology (ASH), the Canadian Agency for Drugs and Technology in Health (CADTH) and the National Institutes of Health and Clinical Excellence (NICE) in the UK have adopted the GRADE system ( www.gradeworkinggroup.org ).

Cochrane has also formally adopted this approach, and all Cochrane Reviews should use GRADE to evaluate the certainty of evidence for important outcomes (see MECIR Box 14.2.a ).

MECIR Box 14.2.a Relevant expectations for conduct of intervention reviews

For systematic reviews, the GRADE approach defines the certainty of a body of evidence as the extent to which one can be confident that an estimate of effect or association is close to the quantity of specific interest. Assessing the certainty of a body of evidence involves consideration of within- and across-study risk of bias (limitations in study design and execution or methodological quality), inconsistency (or heterogeneity), indirectness of evidence, imprecision of the effect estimates and risk of publication bias (see Section 14.2.2 ), as well as domains that may increase our confidence in the effect estimate (as described in Section 14.2.3 ). The GRADE system entails an assessment of the certainty of a body of evidence for each individual outcome. Judgements about the domains that determine the certainty of evidence should be described in the results or discussion section and as part of the ‘Summary of findings’ table.

The GRADE approach specifies four levels of certainty ( Figure 14.2.a ). For interventions, including diagnostic and other tests that are evaluated as interventions (Schünemann et al 2008b, Schünemann et al 2008a, Balshem et al 2011, Schünemann et al 2012), the starting point for rating the certainty of evidence is categorized into two types:

• randomized trials; and
• non-randomized studies of interventions (NRSI), including observational studies (including but not limited to cohort studies, and case-control studies, cross-sectional studies, case series and case reports, although not all of these designs are usually included in Cochrane Reviews).

There are many instances in which review authors rely on information from NRSI, in particular to evaluate potential harms (see Chapter 24 ). In addition, review authors can obtain relevant data from both randomized trials and NRSI, with each type of evidence complementing the other (Schünemann et al 2013).

In GRADE, a body of evidence from randomized trials begins with a high-certainty rating while a body of evidence from NRSI begins with a low-certainty rating. The lower rating with NRSI is the result of the potential bias induced by the lack of randomization (i.e. confounding and selection bias).

However, when using the new Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool (Sterne et al 2016), an assessment tool that covers the risk of bias due to lack of randomization, all studies may start as high certainty of the evidence (Schünemann et al 2018). The approach of starting all study designs (including NRSI) as high certainty does not conflict with the initial GRADE approach of starting the rating of NRSI as low certainty evidence. This is because a body of evidence from NRSI should generally be downgraded by two levels due to the inherent risk of bias associated with the lack of randomization, namely confounding and selection bias. Not downgrading NRSI from high to low certainty needs transparent and detailed justification for what mitigates concerns about confounding and selection bias (Schünemann et al 2018). Very few examples of where not rating down by two levels is appropriate currently exist.

The highest certainty rating is a body of evidence when there are no concerns in any of the GRADE factors listed in Figure 14.2.a . Review authors often downgrade evidence to moderate, low or even very low certainty evidence, depending on the presence of the five factors in Figure 14.2.a . Usually, certainty rating will fall by one level for each factor, up to a maximum of three levels for all factors. If there are very severe problems for any one domain (e.g. when assessing risk of bias, all studies were unconcealed, unblinded and lost over 50% of their patients to follow-up), evidence may fall by two levels due to that factor alone. It is not possible to rate lower than ‘very low certainty’ evidence.

Review authors will generally grade evidence from sound non-randomized studies as low certainty, even if ROBINS-I is used. If, however, such studies yield large effects and there is no obvious bias explaining those effects, review authors may rate the evidence as moderate or – if the effect is large enough – even as high certainty ( Figure 14.2.a ). The very low certainty level is appropriate for, but is not limited to, studies with critical problems and unsystematic clinical observations (e.g. case series or case reports).

Figure 14.2.a Levels of the certainty of a body of evidence in the GRADE approach. *Upgrading criteria are usually applicable to non-randomized studies only (but exceptions exist).

14.2.2 Domains that can lead to decreasing the certainty level of a body of evidence   

We now describe in more detail the five reasons (or domains) for downgrading the certainty of a body of evidence for a specific outcome. In each case, if no reason is found for downgrading the evidence, it should be classified as 'no limitation or not serious' (not important enough to warrant downgrading). If a reason is found for downgrading the evidence, it should be classified as 'serious' (downgrading the certainty rating by one level) or 'very serious' (downgrading the certainty grade by two levels). For non-randomized studies assessed with ROBINS-I, rating down by three levels should be classified as 'extremely' serious.

(1) Risk of bias or limitations in the detailed design and implementation

Our confidence in an estimate of effect decreases if studies suffer from major limitations that are likely to result in a biased assessment of the intervention effect. For randomized trials, these methodological limitations include failure to generate a random sequence, lack of allocation sequence concealment, lack of blinding (particularly with subjective outcomes that are highly susceptible to biased assessment), a large loss to follow-up or selective reporting of outcomes. Chapter 8 provides a discussion of study-level assessments of risk of bias in the context of a Cochrane Review, and proposes an approach to assessing the risk of bias for an outcome across studies as ‘Low’ risk of bias, ‘Some concerns’ and ‘High’ risk of bias for randomized trials. Levels of ‘Low’. ‘Moderate’, ‘Serious’ and ‘Critical’ risk of bias arise for non-randomized studies assessed with ROBINS-I ( Chapter 25 ). These assessments should feed directly into this GRADE domain. In particular, ‘Low’ risk of bias would indicate ‘no limitation’; ‘Some concerns’ would indicate either ‘no limitation’ or ‘serious limitation’; and ‘High’ risk of bias would indicate either ‘serious limitation’ or ‘very serious limitation’. ‘Critical’ risk of bias on ROBINS-I would indicate extremely serious limitations in GRADE. Review authors should use their judgement to decide between alternative categories, depending on the likely magnitude of the potential biases.

Every study addressing a particular outcome will differ, to some degree, in the risk of bias. Review authors should make an overall judgement on whether the certainty of evidence for an outcome warrants downgrading on the basis of study limitations. The assessment of study limitations should apply to the studies contributing to the results in the ‘Summary of findings’ table, rather than to all studies that could potentially be included in the analysis. We have argued in Chapter 7, Section 7.6.2 , that the primary analysis should be restricted to studies at low (or low and unclear) risk of bias where possible.

Table 14.2.a presents the judgements that must be made in going from assessments of the risk of bias to judgements about study limitations for each outcome included in a ‘Summary of findings’ table. A rating of high certainty evidence can be achieved only when most evidence comes from studies that met the criteria for low risk of bias. For example, of the 22 studies addressing the impact of beta-blockers on mortality in patients with heart failure, most probably or certainly used concealed allocation of the sequence, all blinded at least some key groups and follow-up of randomized patients was almost complete (Brophy et al 2001). The certainty of evidence might be downgraded by one level when most of the evidence comes from individual studies either with a crucial limitation for one item, or with some limitations for multiple items. An example of very serious limitations, warranting downgrading by two levels, is provided by evidence on surgery versus conservative treatment in the management of patients with lumbar disc prolapse (Gibson and Waddell 2007). We are uncertain of the benefit of surgery in reducing symptoms after one year or longer, because the one study included in the analysis had inadequate concealment of the allocation sequence and the outcome was assessed using a crude rating by the surgeon without blinding.

(2) Unexplained heterogeneity or inconsistency of results

When studies yield widely differing estimates of effect (heterogeneity or variability in results), investigators should look for robust explanations for that heterogeneity. For instance, drugs may have larger relative effects in sicker populations or when given in larger doses. A detailed discussion of heterogeneity and its investigation is provided in Chapter 10, Section 10.10 and Section 10.11 . If an important modifier exists, with good evidence that important outcomes are different in different subgroups (which would ideally be pre-specified), then a separate ‘Summary of findings’ table may be considered for a separate population. For instance, a separate ‘Summary of findings’ table would be used for carotid endarterectomy in symptomatic patients with high grade stenosis (70% to 99%) in which the intervention is, in the hands of the right surgeons, beneficial, and another (if review authors considered it relevant) for asymptomatic patients with low grade stenosis (less than 30%) in which surgery appears harmful (Orrapin and Rerkasem 2017). When heterogeneity exists and affects the interpretation of results, but review authors are unable to identify a plausible explanation with the data available, the certainty of the evidence decreases.

(3) Indirectness of evidence

Two types of indirectness are relevant. First, a review comparing the effectiveness of alternative interventions (say A and B) may find that randomized trials are available, but they have compared A with placebo and B with placebo. Thus, the evidence is restricted to indirect comparisons between A and B. Where indirect comparisons are undertaken within a network meta-analysis context, GRADE for network meta-analysis should be used (see Chapter 11, Section 11.5 ).

Second, a review may find randomized trials that meet eligibility criteria but address a restricted version of the main review question in terms of population, intervention, comparator or outcomes. For example, suppose that in a review addressing an intervention for secondary prevention of coronary heart disease, most identified studies happened to be in people who also had diabetes. Then the evidence may be regarded as indirect in relation to the broader question of interest because the population is primarily related to people with diabetes. The opposite scenario can equally apply: a review addressing the effect of a preventive strategy for coronary heart disease in people with diabetes may consider studies in people without diabetes to provide relevant, albeit indirect, evidence. This would be particularly likely if investigators had conducted few if any randomized trials in the target population (e.g. people with diabetes). Other sources of indirectness may arise from interventions studied (e.g. if in all included studies a technical intervention was implemented by expert, highly trained specialists in specialist centres, then evidence on the effects of the intervention outside these centres may be indirect), comparators used (e.g. if the comparator groups received an intervention that is less effective than standard treatment in most settings) and outcomes assessed (e.g. indirectness due to surrogate outcomes when data on patient-important outcomes are not available, or when investigators seek data on quality of life but only symptoms are reported). Review authors should make judgements transparent when they believe downgrading is justified, based on differences in anticipated effects in the group of primary interest. Review authors may be aided and increase transparency of their judgements about indirectness if they use Table 14.2.b available in the GRADEpro GDT software (Schünemann et al 2013).

(4) Imprecision of results

When studies include few participants or few events, and thus have wide confidence intervals, review authors can lower their rating of the certainty of the evidence. The confidence intervals included in the ‘Summary of findings’ table will provide readers with information that allows them to make, to some extent, their own rating of precision. Review authors can use a calculation of the optimal information size (OIS) or review information size (RIS), similar to sample size calculations, to make judgements about imprecision (Guyatt et al 2011b, Schünemann 2016). The OIS or RIS is calculated on the basis of the number of participants required for an adequately powered individual study. If the 95% confidence interval excludes a risk ratio (RR) of 1.0, and the total number of events or patients exceeds the OIS criterion, precision is adequate. If the 95% CI includes appreciable benefit or harm (an RR of under 0.75 or over 1.25 is often suggested as a very rough guide) downgrading for imprecision may be appropriate even if OIS criteria are met (Guyatt et al 2011b, Schünemann 2016).

(5) High probability of publication bias

The certainty of evidence level may be downgraded if investigators fail to report studies on the basis of results (typically those that show no effect: publication bias) or outcomes (typically those that may be harmful or for which no effect was observed: selective outcome non-reporting bias). Selective reporting of outcomes from among multiple outcomes measured is assessed at the study level as part of the assessment of risk of bias (see Chapter 8, Section 8.7 ), so for the studies contributing to the outcome in the ‘Summary of findings’ table this is addressed by domain 1 above (limitations in the design and implementation). If a large number of studies included in the review do not contribute to an outcome, or if there is evidence of publication bias, the certainty of the evidence may be downgraded. Chapter 13 provides a detailed discussion of reporting biases, including publication bias, and how it may be tackled in a Cochrane Review. A prototypical situation that may elicit suspicion of publication bias is when published evidence includes a number of small studies, all of which are industry-funded (Bhandari et al 2004). For example, 14 studies of flavanoids in patients with haemorrhoids have shown apparent large benefits, but enrolled a total of only 1432 patients (i.e. each study enrolled relatively few patients) (Alonso-Coello et al 2006). The heavy involvement of sponsors in most of these studies raises questions of whether unpublished studies that suggest no benefit exist (publication bias).

A particular body of evidence can suffer from problems associated with more than one of the five factors listed here, and the greater the problems, the lower the certainty of evidence rating that should result. One could imagine a situation in which randomized trials were available, but all or virtually all of these limitations would be present, and in serious form. A very low certainty of evidence rating would result.

Table 14.2.a Further guidelines for domain 1 (of 5) in a GRADE assessment: going from assessments of risk of bias in studies to judgements about study limitations for main outcomes across studies

Table 14.2.b Judgements about indirectness by outcome (available in GRADEpro GDT)

Intervention:

Comparator:

Direct comparison:

Final judgement about indirectness across domains:

14.2.3 Domains that may lead to increasing the certainty level of a body of evidence

Although NRSI and downgraded randomized trials will generally yield a low rating for certainty of evidence, there will be unusual circumstances in which review authors could ‘upgrade’ such evidence to moderate or even high certainty ( Table 14.3.a ).

• Large effects On rare occasions when methodologically well-done observational studies yield large, consistent and precise estimates of the magnitude of an intervention effect, one may be particularly confident in the results. A large estimated effect (e.g. RR >2 or RR <0.5) in the absence of plausible confounders, or a very large effect (e.g. RR >5 or RR <0.2) in studies with no major threats to validity, might qualify for this. In these situations, while the NRSI may possibly have provided an over-estimate of the true effect, the weak study design may not explain all of the apparent observed benefit. Thus, despite reservations based on the observational study design, review authors are confident that the effect exists. The magnitude of the effect in these studies may move the assigned certainty of evidence from low to moderate (if the effect is large in the absence of other methodological limitations). For example, a meta-analysis of observational studies showed that bicycle helmets reduce the risk of head injuries in cyclists by a large margin (odds ratio (OR) 0.31, 95% CI 0.26 to 0.37) (Thompson et al 2000). This large effect, in the absence of obvious bias that could create the association, suggests a rating of moderate-certainty evidence.  Note : GRADE guidance suggests the possibility of rating up one level for a large effect if the relative effect is greater than 2.0. However, if the point estimate of the relative effect is greater than 2.0, but the confidence interval is appreciably below 2.0, then some hesitation would be appropriate in the decision to rate up for a large effect. Another situation allows inference of a strong association without a formal comparative study. Consider the question of the impact of routine colonoscopy versus no screening for colon cancer on the rate of perforation associated with colonoscopy. Here, a large series of representative patients undergoing colonoscopy may provide high certainty evidence about the risk of perforation associated with colonoscopy. When the risk of the event among patients receiving the relevant comparator is known to be near 0 (i.e. we are certain that the incidence of spontaneous colon perforation in patients not undergoing colonoscopy is extremely low), case series or cohort studies of representative patients can provide high certainty evidence of adverse effects associated with an intervention, thereby allowing us to infer a strong association from even a limited number of events.
• Dose-response The presence of a dose-response gradient may increase our confidence in the findings of observational studies and thereby enhance the assigned certainty of evidence. For example, our confidence in the result of observational studies that show an increased risk of bleeding in patients who have supratherapeutic anticoagulation levels is increased by the observation that there is a dose-response gradient between the length of time needed for blood to clot (as measured by the international normalized ratio (INR)) and an increased risk of bleeding (Levine et al 2004). A systematic review of NRSI investigating the effect of cyclooxygenase-2 inhibitors on cardiovascular events found that the summary estimate (RR) with rofecoxib was 1.33 (95% CI 1.00 to 1.79) with doses less than 25mg/d, and 2.19 (95% CI 1.64 to 2.91) with doses more than 25mg/d. Although residual confounding is likely to exist in the NRSI that address this issue, the existence of a dose-response gradient and the large apparent effect of higher doses of rofecoxib markedly increase our strength of inference that the association cannot be explained by residual confounding, and is therefore likely to be both causal and, at high levels of exposure, substantial.  Note : GRADE guidance suggests the possibility of rating up one level for a large effect if the relative effect is greater than 2.0. Here, the fact that the point estimate of the relative effect is greater than 2.0, but the confidence interval is appreciably below 2.0 might make some hesitate in the decision to rate up for a large effect
• Plausible confounding On occasion, all plausible biases from randomized or non-randomized studies may be working to under-estimate an apparent intervention effect. For example, if only sicker patients receive an experimental intervention or exposure, yet they still fare better, it is likely that the actual intervention or exposure effect is larger than the data suggest. For instance, a rigorous systematic review of observational studies including a total of 38 million patients demonstrated higher death rates in private for-profit versus private not-for-profit hospitals (Devereaux et al 2002). One possible bias relates to different disease severity in patients in the two hospital types. It is likely, however, that patients in the not-for-profit hospitals were sicker than those in the for-profit hospitals. Thus, to the extent that residual confounding existed, it would bias results against the not-for-profit hospitals. The second likely bias was the possibility that higher numbers of patients with excellent private insurance coverage could lead to a hospital having more resources and a spill-over effect that would benefit those without such coverage. Since for-profit hospitals are likely to admit a larger proportion of such well-insured patients than not-for-profit hospitals, the bias is once again against the not-for-profit hospitals. Since the plausible biases would all diminish the demonstrated intervention effect, one might consider the evidence from these observational studies as moderate rather than low certainty. A parallel situation exists when observational studies have failed to demonstrate an association, but all plausible biases would have increased an intervention effect. This situation will usually arise in the exploration of apparent harmful effects. For example, because the hypoglycaemic drug phenformin causes lactic acidosis, the related agent metformin was under suspicion for the same toxicity. Nevertheless, very large observational studies have failed to demonstrate an association (Salpeter et al 2007). Given the likelihood that clinicians would be more alert to lactic acidosis in the presence of the agent and over-report its occurrence, one might consider this moderate, or even high certainty, evidence refuting a causal relationship between typical therapeutic doses of metformin and lactic acidosis.

14.3 Describing the assessment of the certainty of a body of evidence using the GRADE framework

Review authors should report the grading of the certainty of evidence in the Results section for each outcome for which this has been performed, providing the rationale for downgrading or upgrading the evidence, and referring to the ‘Summary of findings’ table where applicable.

Table 14.3.a provides a framework and examples for how review authors can justify their judgements about the certainty of evidence in each domain. These justifications should also be included in explanatory notes to the ‘Summary of Findings’ table (see Section 14.1.6.10 ).

Chapter 15, Section 15.6 , describes in more detail how the overall GRADE assessment across all domains can be used to draw conclusions about the effects of the intervention, as well as providing implications for future research.

Table 14.3.a Framework for describing the certainty of evidence and justifying downgrading or upgrading

14.4 Chapter information

Authors: Holger J Schünemann, Julian PT Higgins, Gunn E Vist, Paul Glasziou, Elie A Akl, Nicole Skoetz, Gordon H Guyatt; on behalf of the Cochrane GRADEing Methods Group (formerly Applicability and Recommendations Methods Group) and the Cochrane Statistical Methods Group

Acknowledgements: Andrew D Oxman contributed to earlier versions. Professor Penny Hawe contributed to the text on adverse effects in earlier versions. Jon Deeks provided helpful contributions on an earlier version of this chapter. For details of previous authors and editors of the Handbook , please refer to the Preface.

Funding: This work was in part supported by funding from the Michael G DeGroote Cochrane Canada Centre and the Ontario Ministry of Health.

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Santesso N, Carrasco-Labra A, Langendam M, Brignardello-Petersen R, Mustafa RA, Heus P, Lasserson T, Opiyo N, Kunnamo I, Sinclair D, Garner P, Treweek S, Tovey D, Akl EA, Tugwell P, Brozek JL, Guyatt G, Schünemann HJ. Improving GRADE evidence tables part 3: detailed guidance for explanatory footnotes supports creating and understanding GRADE certainty in the evidence judgments. Journal of Clinical Epidemiology 2016; 74 : 28-39.

Schünemann HJ, Best D, Vist G, Oxman AD, Group GW. Letters, numbers, symbols and words: how to communicate grades of evidence and recommendations. Canadian Medical Association Journal 2003; 169 : 677-680.

Schünemann HJ, Jaeschke R, Cook DJ, Bria WF, El-Solh AA, Ernst A, Fahy BF, Gould MK, Horan KL, Krishnan JA, Manthous CA, Maurer JR, McNicholas WT, Oxman AD, Rubenfeld G, Turino GM, Guyatt G. An official ATS statement: grading the quality of evidence and strength of recommendations in ATS guidelines and recommendations. American Journal of Respiratory and Critical Care Medicine 2006; 174 : 605-614.

Schünemann HJ, Oxman AD, Brozek J, Glasziou P, Jaeschke R, Vist GE, Williams JW, Jr., Kunz R, Craig J, Montori VM, Bossuyt P, Guyatt GH. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies. BMJ 2008a; 336 : 1106-1110.

Schünemann HJ, Oxman AD, Brozek J, Glasziou P, Bossuyt P, Chang S, Muti P, Jaeschke R, Guyatt GH. GRADE: assessing the quality of evidence for diagnostic recommendations. ACP Journal Club 2008b; 149 : 2.

Schünemann HJ, Mustafa R, Brozek J. [Diagnostic accuracy and linked evidence--testing the chain]. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen 2012; 106 : 153-160.

Schünemann HJ, Tugwell P, Reeves BC, Akl EA, Santesso N, Spencer FA, Shea B, Wells G, Helfand M. Non-randomized studies as a source of complementary, sequential or replacement evidence for randomized controlled trials in systematic reviews on the effects of interventions. Research Synthesis Methods 2013; 4 : 49-62.

Schünemann HJ. Interpreting GRADE's levels of certainty or quality of the evidence: GRADE for statisticians, considering review information size or less emphasis on imprecision? Journal of Clinical Epidemiology 2016; 75 : 6-15.

Schünemann HJ, Cuello C, Akl EA, Mustafa RA, Meerpohl JJ, Thayer K, Morgan RL, Gartlehner G, Kunz R, Katikireddi SV, Sterne J, Higgins JPT, Guyatt G, Group GW. GRADE guidelines: 18. How ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence. Journal of Clinical Epidemiology 2018.

Spencer-Bonilla G, Quinones AR, Montori VM, International Minimally Disruptive Medicine W. Assessing the Burden of Treatment. Journal of General Internal Medicine 2017; 32 : 1141-1145.

Spencer FA, Iorio A, You J, Murad MH, Schünemann HJ, Vandvik PO, Crowther MA, Pottie K, Lang ES, Meerpohl JJ, Falck-Ytter Y, Alonso-Coello P, Guyatt GH. Uncertainties in baseline risk estimates and confidence in treatment effects. BMJ 2012; 345 : e7401.

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For permission to re-use material from the Handbook (either academic or commercial), please see here for full details.

Chapter 1. Introduction

“Science is in danger, and for that reason it is becoming dangerous” -Pierre Bourdieu, Science of Science and Reflexivity

Why an Open Access Textbook on Qualitative Research Methods?

I have been teaching qualitative research methods to both undergraduates and graduate students for many years.  Although there are some excellent textbooks out there, they are often costly, and none of them, to my mind, properly introduces qualitative research methods to the beginning student (whether undergraduate or graduate student).  In contrast, this open-access textbook is designed as a (free) true introduction to the subject, with helpful, practical pointers on how to conduct research and how to access more advanced instruction.  

Textbooks are typically arranged in one of two ways: (1) by technique (each chapter covers one method used in qualitative research); or (2) by process (chapters advance from research design through publication).  But both of these approaches are necessary for the beginner student.  This textbook will have sections dedicated to the process as well as the techniques of qualitative research.  This is a true “comprehensive” book for the beginning student.  In addition to covering techniques of data collection and data analysis, it provides a road map of how to get started and how to keep going and where to go for advanced instruction.  It covers aspects of research design and research communication as well as methods employed.  Along the way, it includes examples from many different disciplines in the social sciences.

The primary goal has been to create a useful, accessible, engaging textbook for use across many disciplines.  And, let’s face it.  Textbooks can be boring.  I hope readers find this to be a little different.  I have tried to write in a practical and forthright manner, with many lively examples and references to good and intellectually creative qualitative research.  Woven throughout the text are short textual asides (in colored textboxes) by professional (academic) qualitative researchers in various disciplines.  These short accounts by practitioners should help inspire students.  So, let’s begin!

What is Research?

When we use the word research , what exactly do we mean by that?  This is one of those words that everyone thinks they understand, but it is worth beginning this textbook with a short explanation.  We use the term to refer to “empirical research,” which is actually a historically specific approach to understanding the world around us.  Think about how you know things about the world. [1] You might know your mother loves you because she’s told you she does.  Or because that is what “mothers” do by tradition.  Or you might know because you’ve looked for evidence that she does, like taking care of you when you are sick or reading to you in bed or working two jobs so you can have the things you need to do OK in life.  Maybe it seems churlish to look for evidence; you just take it “on faith” that you are loved.

Only one of the above comes close to what we mean by research.  Empirical research is research (investigation) based on evidence.  Conclusions can then be drawn from observable data.  This observable data can also be “tested” or checked.  If the data cannot be tested, that is a good indication that we are not doing research.  Note that we can never “prove” conclusively, through observable data, that our mothers love us.  We might have some “disconfirming evidence” (that time she didn’t show up to your graduation, for example) that could push you to question an original hypothesis , but no amount of “confirming evidence” will ever allow us to say with 100% certainty, “my mother loves me.”  Faith and tradition and authority work differently.  Our knowledge can be 100% certain using each of those alternative methods of knowledge, but our certainty in those cases will not be based on facts or evidence.

For many periods of history, those in power have been nervous about “science” because it uses evidence and facts as the primary source of understanding the world, and facts can be at odds with what power or authority or tradition want you to believe.  That is why I say that scientific empirical research is a historically specific approach to understand the world.  You are in college or university now partly to learn how to engage in this historically specific approach.

In the sixteenth and seventeenth centuries in Europe, there was a newfound respect for empirical research, some of which was seriously challenging to the established church.  Using observations and testing them, scientists found that the earth was not at the center of the universe, for example, but rather that it was but one planet of many which circled the sun. [2]   For the next two centuries, the science of astronomy, physics, biology, and chemistry emerged and became disciplines taught in universities.  All used the scientific method of observation and testing to advance knowledge.  Knowledge about people , however, and social institutions, however, was still left to faith, tradition, and authority.  Historians and philosophers and poets wrote about the human condition, but none of them used research to do so. [3]

It was not until the nineteenth century that “social science” really emerged, using the scientific method (empirical observation) to understand people and social institutions.  New fields of sociology, economics, political science, and anthropology emerged.  The first sociologists, people like Auguste Comte and Karl Marx, sought specifically to apply the scientific method of research to understand society, Engels famously claiming that Marx had done for the social world what Darwin did for the natural world, tracings its laws of development.  Today we tend to take for granted the naturalness of science here, but it is actually a pretty recent and radical development.

To return to the question, “does your mother love you?”  Well, this is actually not really how a researcher would frame the question, as it is too specific to your case.  It doesn’t tell us much about the world at large, even if it does tell us something about you and your relationship with your mother.  A social science researcher might ask, “do mothers love their children?”  Or maybe they would be more interested in how this loving relationship might change over time (e.g., “do mothers love their children more now than they did in the 18th century when so many children died before reaching adulthood?”) or perhaps they might be interested in measuring quality of love across cultures or time periods, or even establishing “what love looks like” using the mother/child relationship as a site of exploration.  All of these make good research questions because we can use observable data to answer them.

What is Qualitative Research?

“All we know is how to learn. How to study, how to listen, how to talk, how to tell.  If we don’t tell the world, we don’t know the world.  We’re lost in it, we die.” -Ursula LeGuin, The Telling

At its simplest, qualitative research is research about the social world that does not use numbers in its analyses.  All those who fear statistics can breathe a sigh of relief – there are no mathematical formulae or regression models in this book! But this definition is less about what qualitative research can be and more about what it is not.  To be honest, any simple statement will fail to capture the power and depth of qualitative research.  One way of contrasting qualitative research to quantitative research is to note that the focus of qualitative research is less about explaining and predicting relationships between variables and more about understanding the social world.  To use our mother love example, the question about “what love looks like” is a good question for the qualitative researcher while all questions measuring love or comparing incidences of love (both of which require measurement) are good questions for quantitative researchers. Patton writes,

Qualitative data describe.  They take us, as readers, into the time and place of the observation so that we know what it was like to have been there.  They capture and communicate someone else’s experience of the world in his or her own words.  Qualitative data tell a story. ( Patton 2002:47 )

Qualitative researchers are asking different questions about the world than their quantitative colleagues.  Even when researchers are employed in “mixed methods” research ( both quantitative and qualitative), they are using different methods to address different questions of the study.  I do a lot of research about first-generation and working-college college students.  Where a quantitative researcher might ask, how many first-generation college students graduate from college within four years? Or does first-generation college status predict high student debt loads?  A qualitative researcher might ask, how does the college experience differ for first-generation college students?  What is it like to carry a lot of debt, and how does this impact the ability to complete college on time?  Both sets of questions are important, but they can only be answered using specific tools tailored to those questions.  For the former, you need large numbers to make adequate comparisons.  For the latter, you need to talk to people, find out what they are thinking and feeling, and try to inhabit their shoes for a little while so you can make sense of their experiences and beliefs.

Examples of Qualitative Research

You have probably seen examples of qualitative research before, but you might not have paid particular attention to how they were produced or realized that the accounts you were reading were the result of hours, months, even years of research “in the field.”  A good qualitative researcher will present the product of their hours of work in such a way that it seems natural, even obvious, to the reader.  Because we are trying to convey what it is like answers, qualitative research is often presented as stories – stories about how people live their lives, go to work, raise their children, interact with one another.  In some ways, this can seem like reading particularly insightful novels.  But, unlike novels, there are very specific rules and guidelines that qualitative researchers follow to ensure that the “story” they are telling is accurate , a truthful rendition of what life is like for the people being studied.  Most of this textbook will be spent conveying those rules and guidelines.  Let’s take a look, first, however, at three examples of what the end product looks like.  I have chosen these three examples to showcase very different approaches to qualitative research, and I will return to these five examples throughout the book.  They were all published as whole books (not chapters or articles), and they are worth the long read, if you have the time.  I will also provide some information on how these books came to be and the length of time it takes to get them into book version.  It is important you know about this process, and the rest of this textbook will help explain why it takes so long to conduct good qualitative research!

Example 1 : The End Game (ethnography + interviews)

Corey Abramson is a sociologist who teaches at the University of Arizona.   In 2015 he published The End Game: How Inequality Shapes our Final Years ( 2015 ). This book was based on the research he did for his dissertation at the University of California-Berkeley in 2012.  Actually, the dissertation was completed in 2012 but the work that was produced that took several years.  The dissertation was entitled, “This is How We Live, This is How We Die: Social Stratification, Aging, and Health in Urban America” ( 2012 ).  You can see how the book version, which was written for a more general audience, has a more engaging sound to it, but that the dissertation version, which is what academic faculty read and evaluate, has a more descriptive title.  You can read the title and know that this is a study about aging and health and that the focus is going to be inequality and that the context (place) is going to be “urban America.”  It’s a study about “how” people do something – in this case, how they deal with aging and death.  This is the very first sentence of the dissertation, “From our first breath in the hospital to the day we die, we live in a society characterized by unequal opportunities for maintaining health and taking care of ourselves when ill.  These disparities reflect persistent racial, socio-economic, and gender-based inequalities and contribute to their persistence over time” ( 1 ).  What follows is a truthful account of how that is so.

Cory Abramson spent three years conducting his research in four different urban neighborhoods.  We call the type of research he conducted “comparative ethnographic” because he designed his study to compare groups of seniors as they went about their everyday business.  It’s comparative because he is comparing different groups (based on race, class, gender) and ethnographic because he is studying the culture/way of life of a group. [4]   He had an educated guess, rooted in what previous research had shown and what social theory would suggest, that people’s experiences of aging differ by race, class, and gender.  So, he set up a research design that would allow him to observe differences.  He chose two primarily middle-class (one was racially diverse and the other was predominantly White) and two primarily poor neighborhoods (one was racially diverse and the other was predominantly African American).  He hung out in senior centers and other places seniors congregated, watched them as they took the bus to get prescriptions filled, sat in doctor’s offices with them, and listened to their conversations with each other.  He also conducted more formal conversations, what we call in-depth interviews, with sixty seniors from each of the four neighborhoods.  As with a lot of fieldwork , as he got closer to the people involved, he both expanded and deepened his reach –

By the end of the project, I expanded my pool of general observations to include various settings frequented by seniors: apartment building common rooms, doctors’ offices, emergency rooms, pharmacies, senior centers, bars, parks, corner stores, shopping centers, pool halls, hair salons, coffee shops, and discount stores. Over the course of the three years of fieldwork, I observed hundreds of elders, and developed close relationships with a number of them. ( 2012:10 )

When Abramson rewrote the dissertation for a general audience and published his book in 2015, it got a lot of attention.  It is a beautifully written book and it provided insight into a common human experience that we surprisingly know very little about.  It won the Outstanding Publication Award by the American Sociological Association Section on Aging and the Life Course and was featured in the New York Times .  The book was about aging, and specifically how inequality shapes the aging process, but it was also about much more than that.  It helped show how inequality affects people’s everyday lives.  For example, by observing the difficulties the poor had in setting up appointments and getting to them using public transportation and then being made to wait to see a doctor, sometimes in standing-room-only situations, when they are unwell, and then being treated dismissively by hospital staff, Abramson allowed readers to feel the material reality of being poor in the US.  Comparing these examples with seniors with adequate supplemental insurance who have the resources to hire car services or have others assist them in arranging care when they need it, jolts the reader to understand and appreciate the difference money makes in the lives and circumstances of us all, and in a way that is different than simply reading a statistic (“80% of the poor do not keep regular doctor’s appointments”) does.  Qualitative research can reach into spaces and places that often go unexamined and then reports back to the rest of us what it is like in those spaces and places.

Example 2: Racing for Innocence (Interviews + Content Analysis + Fictional Stories)

Jennifer Pierce is a Professor of American Studies at the University of Minnesota.  Trained as a sociologist, she has written a number of books about gender, race, and power.  Her very first book, Gender Trials: Emotional Lives in Contemporary Law Firms, published in 1995, is a brilliant look at gender dynamics within two law firms.  Pierce was a participant observer, working as a paralegal, and she observed how female lawyers and female paralegals struggled to obtain parity with their male colleagues.

Fifteen years later, she reexamined the context of the law firm to include an examination of racial dynamics, particularly how elite white men working in these spaces created and maintained a culture that made it difficult for both female attorneys and attorneys of color to thrive. Her book, Racing for Innocence: Whiteness, Gender, and the Backlash Against Affirmative Action , published in 2012, is an interesting and creative blending of interviews with attorneys, content analyses of popular films during this period, and fictional accounts of racial discrimination and sexual harassment.  The law firm she chose to study had come under an affirmative action order and was in the process of implementing equitable policies and programs.  She wanted to understand how recipients of white privilege (the elite white male attorneys) come to deny the role they play in reproducing inequality.  Through interviews with attorneys who were present both before and during the affirmative action order, she creates a historical record of the “bad behavior” that necessitated new policies and procedures, but also, and more importantly , probed the participants ’ understanding of this behavior.  It should come as no surprise that most (but not all) of the white male attorneys saw little need for change, and that almost everyone else had accounts that were different if not sometimes downright harrowing.

I’ve used Pierce’s book in my qualitative research methods courses as an example of an interesting blend of techniques and presentation styles.  My students often have a very difficult time with the fictional accounts she includes.  But they serve an important communicative purpose here.  They are her attempts at presenting “both sides” to an objective reality – something happens (Pierce writes this something so it is very clear what it is), and the two participants to the thing that happened have very different understandings of what this means.  By including these stories, Pierce presents one of her key findings – people remember things differently and these different memories tend to support their own ideological positions.  I wonder what Pierce would have written had she studied the murder of George Floyd or the storming of the US Capitol on January 6 or any number of other historic events whose observers and participants record very different happenings.

This is not to say that qualitative researchers write fictional accounts.  In fact, the use of fiction in our work remains controversial.  When used, it must be clearly identified as a presentation device, as Pierce did.  I include Racing for Innocence here as an example of the multiple uses of methods and techniques and the way that these work together to produce better understandings by us, the readers, of what Pierce studied.  We readers come away with a better grasp of how and why advantaged people understate their own involvement in situations and structures that advantage them.  This is normal human behavior , in other words.  This case may have been about elite white men in law firms, but the general insights here can be transposed to other settings.  Indeed, Pierce argues that more research needs to be done about the role elites play in the reproduction of inequality in the workplace in general.

Example 3: Amplified Advantage (Mixed Methods: Survey Interviews + Focus Groups + Archives)

The final example comes from my own work with college students, particularly the ways in which class background affects the experience of college and outcomes for graduates.  I include it here as an example of mixed methods, and for the use of supplementary archival research.  I’ve done a lot of research over the years on first-generation, low-income, and working-class college students.  I am curious (and skeptical) about the possibility of social mobility today, particularly with the rising cost of college and growing inequality in general.  As one of the few people in my family to go to college, I didn’t grow up with a lot of examples of what college was like or how to make the most of it.  And when I entered graduate school, I realized with dismay that there were very few people like me there.  I worried about becoming too different from my family and friends back home.  And I wasn’t at all sure that I would ever be able to pay back the huge load of debt I was taking on.  And so I wrote my dissertation and first two books about working-class college students.  These books focused on experiences in college and the difficulties of navigating between family and school ( Hurst 2010a, 2012 ).  But even after all that research, I kept coming back to wondering if working-class students who made it through college had an equal chance at finding good jobs and happy lives,

What happens to students after college?  Do working-class students fare as well as their peers?  I knew from my own experience that barriers continued through graduate school and beyond, and that my debtload was higher than that of my peers, constraining some of the choices I made when I graduated.  To answer these questions, I designed a study of students attending small liberal arts colleges, the type of college that tried to equalize the experience of students by requiring all students to live on campus and offering small classes with lots of interaction with faculty.  These private colleges tend to have more money and resources so they can provide financial aid to low-income students.  They also attract some very wealthy students.  Because they enroll students across the class spectrum, I would be able to draw comparisons.  I ended up spending about four years collecting data, both a survey of more than 2000 students (which formed the basis for quantitative analyses) and qualitative data collection (interviews, focus groups, archival research, and participant observation).  This is what we call a “mixed methods” approach because we use both quantitative and qualitative data.  The survey gave me a large enough number of students that I could make comparisons of the how many kind, and to be able to say with some authority that there were in fact significant differences in experience and outcome by class (e.g., wealthier students earned more money and had little debt; working-class students often found jobs that were not in their chosen careers and were very affected by debt, upper-middle-class students were more likely to go to graduate school).  But the survey analyses could not explain why these differences existed.  For that, I needed to talk to people and ask them about their motivations and aspirations.  I needed to understand their perceptions of the world, and it is very hard to do this through a survey.

By interviewing students and recent graduates, I was able to discern particular patterns and pathways through college and beyond.  Specifically, I identified three versions of gameplay.  Upper-middle-class students, whose parents were themselves professionals (academics, lawyers, managers of non-profits), saw college as the first stage of their education and took classes and declared majors that would prepare them for graduate school.  They also spent a lot of time building their resumes, taking advantage of opportunities to help professors with their research, or study abroad.  This helped them gain admission to highly-ranked graduate schools and interesting jobs in the public sector.  In contrast, upper-class students, whose parents were wealthy and more likely to be engaged in business (as CEOs or other high-level directors), prioritized building social capital.  They did this by joining fraternities and sororities and playing club sports.  This helped them when they graduated as they called on friends and parents of friends to find them well-paying jobs.  Finally, low-income, first-generation, and working-class students were often adrift.  They took the classes that were recommended to them but without the knowledge of how to connect them to life beyond college.  They spent time working and studying rather than partying or building their resumes.  All three sets of students thought they were “doing college” the right way, the way that one was supposed to do college.   But these three versions of gameplay led to distinct outcomes that advantaged some students over others.  I titled my work “Amplified Advantage” to highlight this process.

These three examples, Cory Abramson’s The End Game , Jennifer Peirce’s Racing for Innocence, and my own Amplified Advantage, demonstrate the range of approaches and tools available to the qualitative researcher.  They also help explain why qualitative research is so important.  Numbers can tell us some things about the world, but they cannot get at the hearts and minds, motivations and beliefs of the people who make up the social worlds we inhabit.  For that, we need tools that allow us to listen and make sense of what people tell us and show us.  That is what good qualitative research offers us.

How Is This Book Organized?

This textbook is organized as a comprehensive introduction to the use of qualitative research methods.  The first half covers general topics (e.g., approaches to qualitative research, ethics) and research design (necessary steps for building a successful qualitative research study).  The second half reviews various data collection and data analysis techniques.  Of course, building a successful qualitative research study requires some knowledge of data collection and data analysis so the chapters in the first half and the chapters in the second half should be read in conversation with each other.  That said, each chapter can be read on its own for assistance with a particular narrow topic.  In addition to the chapters, a helpful glossary can be found in the back of the book.  Rummage around in the text as needed.

Chapter Descriptions

Chapter 2 provides an overview of the Research Design Process.  How does one begin a study? What is an appropriate research question?  How is the study to be done – with what methods ?  Involving what people and sites?  Although qualitative research studies can and often do change and develop over the course of data collection, it is important to have a good idea of what the aims and goals of your study are at the outset and a good plan of how to achieve those aims and goals.  Chapter 2 provides a road map of the process.

Chapter 3 describes and explains various ways of knowing the (social) world.  What is it possible for us to know about how other people think or why they behave the way they do?  What does it mean to say something is a “fact” or that it is “well-known” and understood?  Qualitative researchers are particularly interested in these questions because of the types of research questions we are interested in answering (the how questions rather than the how many questions of quantitative research).  Qualitative researchers have adopted various epistemological approaches.  Chapter 3 will explore these approaches, highlighting interpretivist approaches that acknowledge the subjective aspect of reality – in other words, reality and knowledge are not objective but rather influenced by (interpreted through) people.

Chapter 4 focuses on the practical matter of developing a research question and finding the right approach to data collection.  In any given study (think of Cory Abramson’s study of aging, for example), there may be years of collected data, thousands of observations , hundreds of pages of notes to read and review and make sense of.  If all you had was a general interest area (“aging”), it would be very difficult, nearly impossible, to make sense of all of that data.  The research question provides a helpful lens to refine and clarify (and simplify) everything you find and collect.  For that reason, it is important to pull out that lens (articulate the research question) before you get started.  In the case of the aging study, Cory Abramson was interested in how inequalities affected understandings and responses to aging.  It is for this reason he designed a study that would allow him to compare different groups of seniors (some middle-class, some poor).  Inevitably, he saw much more in the three years in the field than what made it into his book (or dissertation), but he was able to narrow down the complexity of the social world to provide us with this rich account linked to the original research question.  Developing a good research question is thus crucial to effective design and a successful outcome.  Chapter 4 will provide pointers on how to do this.  Chapter 4 also provides an overview of general approaches taken to doing qualitative research and various “traditions of inquiry.”

Chapter 5 explores sampling .  After you have developed a research question and have a general idea of how you will collect data (Observations?  Interviews?), how do you go about actually finding people and sites to study?  Although there is no “correct number” of people to interview , the sample should follow the research question and research design.  Unlike quantitative research, qualitative research involves nonprobability sampling.  Chapter 5 explains why this is so and what qualities instead make a good sample for qualitative research.

Chapter 6 addresses the importance of reflexivity in qualitative research.  Related to epistemological issues of how we know anything about the social world, qualitative researchers understand that we the researchers can never be truly neutral or outside the study we are conducting.  As observers, we see things that make sense to us and may entirely miss what is either too obvious to note or too different to comprehend.  As interviewers, as much as we would like to ask questions neutrally and remain in the background, interviews are a form of conversation, and the persons we interview are responding to us .  Therefore, it is important to reflect upon our social positions and the knowledges and expectations we bring to our work and to work through any blind spots that we may have.  Chapter 6 provides some examples of reflexivity in practice and exercises for thinking through one’s own biases.

Chapter 7 is a very important chapter and should not be overlooked.  As a practical matter, it should also be read closely with chapters 6 and 8.  Because qualitative researchers deal with people and the social world, it is imperative they develop and adhere to a strong ethical code for conducting research in a way that does not harm.  There are legal requirements and guidelines for doing so (see chapter 8), but these requirements should not be considered synonymous with the ethical code required of us.   Each researcher must constantly interrogate every aspect of their research, from research question to design to sample through analysis and presentation, to ensure that a minimum of harm (ideally, zero harm) is caused.  Because each research project is unique, the standards of care for each study are unique.  Part of being a professional researcher is carrying this code in one’s heart, being constantly attentive to what is required under particular circumstances.  Chapter 7 provides various research scenarios and asks readers to weigh in on the suitability and appropriateness of the research.  If done in a class setting, it will become obvious fairly quickly that there are often no absolutely correct answers, as different people find different aspects of the scenarios of greatest importance.  Minimizing the harm in one area may require possible harm in another.  Being attentive to all the ethical aspects of one’s research and making the best judgments one can, clearly and consciously, is an integral part of being a good researcher.

Chapter 8 , best to be read in conjunction with chapter 7, explains the role and importance of Institutional Review Boards (IRBs) .  Under federal guidelines, an IRB is an appropriately constituted group that has been formally designated to review and monitor research involving human subjects .  Every institution that receives funding from the federal government has an IRB.  IRBs have the authority to approve, require modifications to (to secure approval), or disapprove research.  This group review serves an important role in the protection of the rights and welfare of human research subjects.  Chapter 8 reviews the history of IRBs and the work they do but also argues that IRBs’ review of qualitative research is often both over-inclusive and under-inclusive.  Some aspects of qualitative research are not well understood by IRBs, given that they were developed to prevent abuses in biomedical research.  Thus, it is important not to rely on IRBs to identify all the potential ethical issues that emerge in our research (see chapter 7).

Chapter 9 provides help for getting started on formulating a research question based on gaps in the pre-existing literature.  Research is conducted as part of a community, even if particular studies are done by single individuals (or small teams).  What any of us finds and reports back becomes part of a much larger body of knowledge.  Thus, it is important that we look at the larger body of knowledge before we actually start our bit to see how we can best contribute.  When I first began interviewing working-class college students, there was only one other similar study I could find, and it hadn’t been published (it was a dissertation of students from poor backgrounds).  But there had been a lot published by professors who had grown up working class and made it through college despite the odds.  These accounts by “working-class academics” became an important inspiration for my study and helped me frame the questions I asked the students I interviewed.  Chapter 9 will provide some pointers on how to search for relevant literature and how to use this to refine your research question.

Chapter 10 serves as a bridge between the two parts of the textbook, by introducing techniques of data collection.  Qualitative research is often characterized by the form of data collection – for example, an ethnographic study is one that employs primarily observational data collection for the purpose of documenting and presenting a particular culture or ethnos.  Techniques can be effectively combined, depending on the research question and the aims and goals of the study.   Chapter 10 provides a general overview of all the various techniques and how they can be combined.

The second part of the textbook moves into the doing part of qualitative research once the research question has been articulated and the study designed.  Chapters 11 through 17 cover various data collection techniques and approaches.  Chapters 18 and 19 provide a very simple overview of basic data analysis.  Chapter 20 covers communication of the data to various audiences, and in various formats.

Chapter 11 begins our overview of data collection techniques with a focus on interviewing , the true heart of qualitative research.  This technique can serve as the primary and exclusive form of data collection, or it can be used to supplement other forms (observation, archival).  An interview is distinct from a survey, where questions are asked in a specific order and often with a range of predetermined responses available.  Interviews can be conversational and unstructured or, more conventionally, semistructured , where a general set of interview questions “guides” the conversation.  Chapter 11 covers the basics of interviews: how to create interview guides, how many people to interview, where to conduct the interview, what to watch out for (how to prepare against things going wrong), and how to get the most out of your interviews.

Chapter 12 covers an important variant of interviewing, the focus group.  Focus groups are semistructured interviews with a group of people moderated by a facilitator (the researcher or researcher’s assistant).  Focus groups explicitly use group interaction to assist in the data collection.  They are best used to collect data on a specific topic that is non-personal and shared among the group.  For example, asking a group of college students about a common experience such as taking classes by remote delivery during the pandemic year of 2020.  Chapter 12 covers the basics of focus groups: when to use them, how to create interview guides for them, and how to run them effectively.

Chapter 13 moves away from interviewing to the second major form of data collection unique to qualitative researchers – observation .  Qualitative research that employs observation can best be understood as falling on a continuum of “fly on the wall” observation (e.g., observing how strangers interact in a doctor’s waiting room) to “participant” observation, where the researcher is also an active participant of the activity being observed.  For example, an activist in the Black Lives Matter movement might want to study the movement, using her inside position to gain access to observe key meetings and interactions.  Chapter  13 covers the basics of participant observation studies: advantages and disadvantages, gaining access, ethical concerns related to insider/outsider status and entanglement, and recording techniques.

Chapter 14 takes a closer look at “deep ethnography” – immersion in the field of a particularly long duration for the purpose of gaining a deeper understanding and appreciation of a particular culture or social world.  Clifford Geertz called this “deep hanging out.”  Whereas participant observation is often combined with semistructured interview techniques, deep ethnography’s commitment to “living the life” or experiencing the situation as it really is demands more conversational and natural interactions with people.  These interactions and conversations may take place over months or even years.  As can be expected, there are some costs to this technique, as well as some very large rewards when done competently.  Chapter 14 provides some examples of deep ethnographies that will inspire some beginning researchers and intimidate others.

Chapter 15 moves in the opposite direction of deep ethnography, a technique that is the least positivist of all those discussed here, to mixed methods , a set of techniques that is arguably the most positivist .  A mixed methods approach combines both qualitative data collection and quantitative data collection, commonly by combining a survey that is analyzed statistically (e.g., cross-tabs or regression analyses of large number probability samples) with semi-structured interviews.  Although it is somewhat unconventional to discuss mixed methods in textbooks on qualitative research, I think it is important to recognize this often-employed approach here.  There are several advantages and some disadvantages to taking this route.  Chapter 16 will describe those advantages and disadvantages and provide some particular guidance on how to design a mixed methods study for maximum effectiveness.

Chapter 16 covers data collection that does not involve live human subjects at all – archival and historical research (chapter 17 will also cover data that does not involve interacting with human subjects).  Sometimes people are unavailable to us, either because they do not wish to be interviewed or observed (as is the case with many “elites”) or because they are too far away, in both place and time.  Fortunately, humans leave many traces and we can often answer questions we have by examining those traces.  Special collections and archives can be goldmines for social science research.  This chapter will explain how to access these places, for what purposes, and how to begin to make sense of what you find.

Chapter 17 covers another data collection area that does not involve face-to-face interaction with humans: content analysis .  Although content analysis may be understood more properly as a data analysis technique, the term is often used for the entire approach, which will be the case here.  Content analysis involves interpreting meaning from a body of text.  This body of text might be something found in historical records (see chapter 16) or something collected by the researcher, as in the case of comment posts on a popular blog post.  I once used the stories told by student loan debtors on the website studentloanjustice.org as the content I analyzed.  Content analysis is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest.  In other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue.  This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis.

Where chapter 17 has pushed us towards data analysis, chapters 18 and 19 are all about what to do with the data collected, whether that data be in the form of interview transcripts or fieldnotes from observations.  Chapter 18 introduces the basics of coding , the iterative process of assigning meaning to the data in order to both simplify and identify patterns.  What is a code and how does it work?  What are the different ways of coding data, and when should you use them?  What is a codebook, and why do you need one?  What does the process of data analysis look like?

Chapter 19 goes further into detail on codes and how to use them, particularly the later stages of coding in which our codes are refined, simplified, combined, and organized.  These later rounds of coding are essential to getting the most out of the data we’ve collected.  As students are often overwhelmed with the amount of data (a corpus of interview transcripts typically runs into the hundreds of pages; fieldnotes can easily top that), this chapter will also address time management and provide suggestions for dealing with chaos and reminders that feeling overwhelmed at the analysis stage is part of the process.  By the end of the chapter, you should understand how “findings” are actually found.

The book concludes with a chapter dedicated to the effective presentation of data results.  Chapter 20 covers the many ways that researchers communicate their studies to various audiences (academic, personal, political), what elements must be included in these various publications, and the hallmarks of excellent qualitative research that various audiences will be expecting.  Because qualitative researchers are motivated by understanding and conveying meaning , effective communication is not only an essential skill but a fundamental facet of the entire research project.  Ethnographers must be able to convey a certain sense of verisimilitude , the appearance of true reality.  Those employing interviews must faithfully depict the key meanings of the people they interviewed in a way that rings true to those people, even if the end result surprises them.  And all researchers must strive for clarity in their publications so that various audiences can understand what was found and why it is important.

The book concludes with a short chapter ( chapter 21 ) discussing the value of qualitative research. At the very end of this book, you will find a glossary of terms. I recommend you make frequent use of the glossary and add to each entry as you find examples. Although the entries are meant to be simple and clear, you may also want to paraphrase the definition—make it “make sense” to you, in other words. In addition to the standard reference list (all works cited here), you will find various recommendations for further reading at the end of many chapters. Some of these recommendations will be examples of excellent qualitative research, indicated with an asterisk (*) at the end of the entry. As they say, a picture is worth a thousand words. A good example of qualitative research can teach you more about conducting research than any textbook can (this one included). I highly recommend you select one to three examples from these lists and read them along with the textbook.

A final note on the choice of examples – you will note that many of the examples used in the text come from research on college students.  This is for two reasons.  First, as most of my research falls in this area, I am most familiar with this literature and have contacts with those who do research here and can call upon them to share their stories with you.  Second, and more importantly, my hope is that this textbook reaches a wide audience of beginning researchers who study widely and deeply across the range of what can be known about the social world (from marine resources management to public policy to nursing to political science to sexuality studies and beyond).  It is sometimes difficult to find examples that speak to all those research interests, however. A focus on college students is something that all readers can understand and, hopefully, appreciate, as we are all now or have been at some point a college student.

Recommended Reading: Other Qualitative Research Textbooks

I’ve included a brief list of some of my favorite qualitative research textbooks and guidebooks if you need more than what you will find in this introductory text.  For each, I’ve also indicated if these are for “beginning” or “advanced” (graduate-level) readers.  Many of these books have several editions that do not significantly vary; the edition recommended is merely the edition I have used in teaching and to whose page numbers any specific references made in the text agree.

Barbour, Rosaline. 2014. Introducing Qualitative Research: A Student’s Guide. Thousand Oaks, CA: SAGE.  A good introduction to qualitative research, with abundant examples (often from the discipline of health care) and clear definitions.  Includes quick summaries at the ends of each chapter.  However, some US students might find the British context distracting and can be a bit advanced in some places.  Beginning .

Bloomberg, Linda Dale, and Marie F. Volpe. 2012. Completing Your Qualitative Dissertation . 2nd ed. Thousand Oaks, CA: SAGE.  Specifically designed to guide graduate students through the research process. Advanced .

Creswell, John W., and Cheryl Poth. 2018 Qualitative Inquiry and Research Design: Choosing among Five Traditions .  4th ed. Thousand Oaks, CA: SAGE.  This is a classic and one of the go-to books I used myself as a graduate student.  One of the best things about this text is its clear presentation of five distinct traditions in qualitative research.  Despite the title, this reasonably sized book is about more than research design, including both data analysis and how to write about qualitative research.  Advanced .

Lareau, Annette. 2021. Listening to People: A Practical Guide to Interviewing, Participant Observation, Data Analysis, and Writing It All Up .  Chicago: University of Chicago Press. A readable and personal account of conducting qualitative research by an eminent sociologist, with a heavy emphasis on the kinds of participant-observation research conducted by the author.  Despite its reader-friendliness, this is really a book targeted to graduate students learning the craft.  Advanced .

Lune, Howard, and Bruce L. Berg. 2018. 9th edition.  Qualitative Research Methods for the Social Sciences.  Pearson . Although a good introduction to qualitative methods, the authors favor symbolic interactionist and dramaturgical approaches, which limits the appeal primarily to sociologists.  Beginning .

Marshall, Catherine, and Gretchen B. Rossman. 2016. 6th edition. Designing Qualitative Research. Thousand Oaks, CA: SAGE.  Very readable and accessible guide to research design by two educational scholars.  Although the presentation is sometimes fairly dry, personal vignettes and illustrations enliven the text.  Beginning .

Maxwell, Joseph A. 2013. Qualitative Research Design: An Interactive Approach .  3rd ed. Thousand Oaks, CA: SAGE. A short and accessible introduction to qualitative research design, particularly helpful for graduate students contemplating theses and dissertations. This has been a standard textbook in my graduate-level courses for years.  Advanced .

Patton, Michael Quinn. 2002. Qualitative Research and Evaluation Methods . Thousand Oaks, CA: SAGE.  This is a comprehensive text that served as my “go-to” reference when I was a graduate student.  It is particularly helpful for those involved in program evaluation and other forms of evaluation studies and uses examples from a wide range of disciplines.  Advanced .

Rubin, Ashley T. 2021. Rocking Qualitative Social Science: An Irreverent Guide to Rigorous Research. Stanford : Stanford University Press.  A delightful and personal read.  Rubin uses rock climbing as an extended metaphor for learning how to conduct qualitative research.  A bit slanted toward ethnographic and archival methods of data collection, with frequent examples from her own studies in criminology. Beginning .

Weis, Lois, and Michelle Fine. 2000. Speed Bumps: A Student-Friendly Guide to Qualitative Research . New York: Teachers College Press.  Readable and accessibly written in a quasi-conversational style.  Particularly strong in its discussion of ethical issues throughout the qualitative research process.  Not comprehensive, however, and very much tied to ethnographic research.  Although designed for graduate students, this is a recommended read for students of all levels.  Beginning .

Patton’s Ten Suggestions for Doing Qualitative Research

The following ten suggestions were made by Michael Quinn Patton in his massive textbooks Qualitative Research and Evaluations Methods . This book is highly recommended for those of you who want more than an introduction to qualitative methods. It is the book I relied on heavily when I was a graduate student, although it is much easier to “dip into” when necessary than to read through as a whole. Patton is asked for “just one bit of advice” for a graduate student considering using qualitative research methods for their dissertation.  Here are his top ten responses, in short form, heavily paraphrased, and with additional comments and emphases from me:

• Make sure that a qualitative approach fits the research question. The following are the kinds of questions that call out for qualitative methods or where qualitative methods are particularly appropriate: questions about people’s experiences or how they make sense of those experiences; studying a person in their natural environment; researching a phenomenon so unknown that it would be impossible to study it with standardized instruments or other forms of quantitative data collection.
• Study qualitative research by going to the original sources for the design and analysis appropriate to the particular approach you want to take (e.g., read Glaser and Straus if you are using grounded theory )
• Find a dissertation adviser who understands or at least who will support your use of qualitative research methods. You are asking for trouble if your entire committee is populated by quantitative researchers, even if they are all very knowledgeable about the subject or focus of your study (maybe even more so if they are!)
• Really work on design. Doing qualitative research effectively takes a lot of planning.  Even if things are more flexible than in quantitative research, a good design is absolutely essential when starting out.
• Practice data collection techniques, particularly interviewing and observing. There is definitely a set of learned skills here!  Do not expect your first interview to be perfect.  You will continue to grow as a researcher the more interviews you conduct, and you will probably come to understand yourself a bit more in the process, too.  This is not easy, despite what others who don’t work with qualitative methods may assume (and tell you!)
• Have a plan for analysis before you begin data collection. This is often a requirement in IRB protocols , although you can get away with writing something fairly simple.  And even if you are taking an approach, such as grounded theory, that pushes you to remain fairly open-minded during the data collection process, you still want to know what you will be doing with all the data collected – creating a codebook? Writing analytical memos? Comparing cases?  Having a plan in hand will also help prevent you from collecting too much extraneous data.
• Be prepared to confront controversies both within the qualitative research community and between qualitative research and quantitative research. Don’t be naïve about this – qualitative research, particularly some approaches, will be derided by many more “positivist” researchers and audiences.  For example, is an “n” of 1 really sufficient?  Yes!  But not everyone will agree.
• Do not make the mistake of using qualitative research methods because someone told you it was easier, or because you are intimidated by the math required of statistical analyses. Qualitative research is difficult in its own way (and many would claim much more time-consuming than quantitative research).  Do it because you are convinced it is right for your goals, aims, and research questions.
• Find a good support network. This could be a research mentor, or it could be a group of friends or colleagues who are also using qualitative research, or it could be just someone who will listen to you work through all of the issues you will confront out in the field and during the writing process.  Even though qualitative research often involves human subjects, it can be pretty lonely.  A lot of times you will feel like you are working without a net.  You have to create one for yourself.  Take care of yourself.
• And, finally, in the words of Patton, “Prepare to be changed. Looking deeply at other people’s lives will force you to look deeply at yourself.”
• We will actually spend an entire chapter ( chapter 3 ) looking at this question in much more detail! ↵
• Note that this might have been news to Europeans at the time, but many other societies around the world had also come to this conclusion through observation.  There is often a tendency to equate “the scientific revolution” with the European world in which it took place, but this is somewhat misleading. ↵
• Historians are a special case here.  Historians have scrupulously and rigorously investigated the social world, but not for the purpose of understanding general laws about how things work, which is the point of scientific empirical research.  History is often referred to as an idiographic field of study, meaning that it studies things that happened or are happening in themselves and not for general observations or conclusions. ↵
• Don’t worry, we’ll spend more time later in this book unpacking the meaning of ethnography and other terms that are important here.  Note the available glossary ↵

An approach to research that is “multimethod in focus, involving an interpretative, naturalistic approach to its subject matter.  This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.  Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives." ( Denzin and Lincoln 2005:2 ). Contrast with quantitative research .

In contrast to methodology, methods are more simply the practices and tools used to collect and analyze data.  Examples of common methods in qualitative research are interviews , observations , and documentary analysis .  One’s methodology should connect to one’s choice of methods, of course, but they are distinguishable terms.  See also methodology .

A proposed explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation.  The positing of a hypothesis is often the first step in quantitative research but not in qualitative research.  Even when qualitative researchers offer possible explanations in advance of conducting research, they will tend to not use the word “hypothesis” as it conjures up the kind of positivist research they are not conducting.

The foundational question to be addressed by the research study.  This will form the anchor of the research design, collection, and analysis.  Note that in qualitative research, the research question may, and probably will, alter or develop during the course of the research.

An approach to research that collects and analyzes numerical data for the purpose of finding patterns and averages, making predictions, testing causal relationships, and generalizing results to wider populations.  Contrast with qualitative research .

Data collection that takes place in real-world settings, referred to as “the field;” a key component of much Grounded Theory and ethnographic research.  Patton ( 2002 ) calls fieldwork “the central activity of qualitative inquiry” where “‘going into the field’ means having direct and personal contact with people under study in their own environments – getting close to people and situations being studied to personally understand the realities of minutiae of daily life” (48).

The people who are the subjects of a qualitative study.  In interview-based studies, they may be the respondents to the interviewer; for purposes of IRBs, they are often referred to as the human subjects of the research.

The branch of philosophy concerned with knowledge.  For researchers, it is important to recognize and adopt one of the many distinguishing epistemological perspectives as part of our understanding of what questions research can address or fully answer.  See, e.g., constructivism , subjectivism, and  objectivism .

An approach that refutes the possibility of neutrality in social science research.  All research is “guided by a set of beliefs and feelings about the world and how it should be understood and studied” (Denzin and Lincoln 2005: 13).  In contrast to positivism , interpretivism recognizes the social constructedness of reality, and researchers adopting this approach focus on capturing interpretations and understandings people have about the world rather than “the world” as it is (which is a chimera).

The cluster of data-collection tools and techniques that involve observing interactions between people, the behaviors, and practices of individuals (sometimes in contrast to what they say about how they act and behave), and cultures in context.  Observational methods are the key tools employed by ethnographers and Grounded Theory .

Research based on data collected and analyzed by the research (in contrast to secondary “library” research).

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

A method of data collection in which the researcher asks the participant questions; the answers to these questions are often recorded and transcribed verbatim. There are many different kinds of interviews - see also semistructured interview , structured interview , and unstructured interview .

The specific group of individuals that you will collect data from.  Contrast population.

The practice of being conscious of and reflective upon one’s own social location and presence when conducting research.  Because qualitative research often requires interaction with live humans, failing to take into account how one’s presence and prior expectations and social location affect the data collected and how analyzed may limit the reliability of the findings.  This remains true even when dealing with historical archives and other content.  Who we are matters when asking questions about how people experience the world because we, too, are a part of that world.

The science and practice of right conduct; in research, it is also the delineation of moral obligations towards research participants, communities to which we belong, and communities in which we conduct our research.

An administrative body established to protect the rights and welfare of human research subjects recruited to participate in research activities conducted under the auspices of the institution with which it is affiliated. The IRB is charged with the responsibility of reviewing all research involving human participants. The IRB is concerned with protecting the welfare, rights, and privacy of human subjects. The IRB has the authority to approve, disapprove, monitor, and require modifications in all research activities that fall within its jurisdiction as specified by both the federal regulations and institutional policy.

Research, according to US federal guidelines, that involves “a living individual about whom an investigator (whether professional or student) conducting research:  (1) Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or  (2) Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.”

One of the primary methodological traditions of inquiry in qualitative research, ethnography is the study of a group or group culture, largely through observational fieldwork supplemented by interviews. It is a form of fieldwork that may include participant-observation data collection. See chapter 14 for a discussion of deep ethnography. 

A form of interview that follows a standard guide of questions asked, although the order of the questions may change to match the particular needs of each individual interview subject, and probing “follow-up” questions are often added during the course of the interview.  The semi-structured interview is the primary form of interviewing used by qualitative researchers in the social sciences.  It is sometimes referred to as an “in-depth” interview.  See also interview and  interview guide .

A method of observational data collection taking place in a natural setting; a form of fieldwork .  The term encompasses a continuum of relative participation by the researcher (from full participant to “fly-on-the-wall” observer).  This is also sometimes referred to as ethnography , although the latter is characterized by a greater focus on the culture under observation.

A research design that employs both quantitative and qualitative methods, as in the case of a survey supplemented by interviews.

An epistemological perspective that posits the existence of reality through sensory experience similar to empiricism but goes further in denying any non-sensory basis of thought or consciousness.  In the social sciences, the term has roots in the proto-sociologist August Comte, who believed he could discern “laws” of society similar to the laws of natural science (e.g., gravity).  The term has come to mean the kinds of measurable and verifiable science conducted by quantitative researchers and is thus used pejoratively by some qualitative researchers interested in interpretation, consciousness, and human understanding.  Calling someone a “positivist” is often intended as an insult.  See also empiricism and objectivism.

A place or collection containing records, documents, or other materials of historical interest; most universities have an archive of material related to the university’s history, as well as other “special collections” that may be of interest to members of the community.

A method of both data collection and data analysis in which a given content (textual, visual, graphic) is examined systematically and rigorously to identify meanings, themes, patterns and assumptions.  Qualitative content analysis (QCA) is concerned with gathering and interpreting an existing body of material.    

A word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data (Saldaña 2021:5).

Usually a verbatim written record of an interview or focus group discussion.

The primary form of data for fieldwork , participant observation , and ethnography .  These notes, taken by the researcher either during the course of fieldwork or at day’s end, should include as many details as possible on what was observed and what was said.  They should include clear identifiers of date, time, setting, and names (or identifying characteristics) of participants.

The process of labeling and organizing qualitative data to identify different themes and the relationships between them; a way of simplifying data to allow better management and retrieval of key themes and illustrative passages.  See coding frame and  codebook.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

A detailed description of any proposed research that involves human subjects for review by IRB.  The protocol serves as the recipe for the conduct of the research activity.  It includes the scientific rationale to justify the conduct of the study, the information necessary to conduct the study, the plan for managing and analyzing the data, and a discussion of the research ethical issues relevant to the research.  Protocols for qualitative research often include interview guides, all documents related to recruitment, informed consent forms, very clear guidelines on the safekeeping of materials collected, and plans for de-identifying transcripts or other data that include personal identifying information.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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84 Comments

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Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

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Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

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Hi Derek, What other theories/methods would you recommend when the data is a whole speech?

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choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

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Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

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do you have any material on Data collection

What a powerful explanation of the QDA methods. Thank you.

Great explanation both written and Video. i have been using of it on a day to day working of my thesis project in accounting and finance. Thank you very much for your support.

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Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

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• Open access
• Published: 15 April 2024

How would you describe a mentally healthy college student based on Chinese culture? A qualitative research from the perspective of college students

• Mingjia Guo 1 ,
• Xiaoming Jia 1 &
• Wenqian Wang 1  

BMC Psychology volume  12 , Article number:  207 ( 2024 ) Cite this article

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Promoting college students’ mental health remains a significant concern, necessitating a clear understanding of what constitutes good mental health. Variations in the conceptualizations of mental health across cultures, typically derived from academic and authoritative perspectives, have overlooked insights from laypeople. This study aims to investigate the characteristics of mentally healthy college students within Chinese cultural contexts, emphasizing perspectives provided by college students themselves.

Undergraduates with self-reported mental health scores ≥ 7 were randomly selected for in-depth interviews. The sample ( N  = 17, 59% female) had a mean age of 20.82 ± 1.33 years and represented diverse regions, backgrounds, and academic fields. Thematic analysis was used in the analysis of the qualitative data, involving initial coding to identify 168 manifestations of mental health among college students, followed by categorizing them into 18 characteristics through focused coding. These characteristics were then organized into five themes via core coding. The Delphi method was utilized to validate the themes with 3 experts, ensuring the trustworthiness of the final findings.

Eighteen characteristics of mentally healthy college students emerged from the interviews, categorized into 5 themes: (1)Value Pursuit (i.e. Having a sense of responsibility and mission and being willing to dedicate oneself to the country at any time.); (2)Life Attitude (i.e. Staying positive and having the ability and quality to cope with hardships.); (3)Interpersonal Ideals (i.e., Showing filial respect to parents appropriately.); (4)Behavior Ability(i.e., Studying diligently and learning well.); and (5)Self-cultivation (i.e., Possessing good qualities advocated by Confucianism, Buddhism, and Taoism coexist harmoniously.). Most of these characteristics directly reflect traditional Chinese culture or culture that has changed with the times. At the same time, some are a reflection of modern Chinese new culture.

Conclusions

On the whole, the characteristics of mentally healthy college students are diverse and with rich connotations, focusing on the individual’s relationship with the country, family, and others, and are good expressions of Chinese cultural features, such as the balance of Yin and Yang, the coexistence of Confucianism, Buddhism, and Taoism, and highlight moral attributes. In essence, these traits hold profound importance in advancing the mental health of Chinese college students.

Peer Review reports

The period of undergraduate study is vital for individual development, physical and mental growth, knowledge reserve, and health literacy development. For undergraduate students, they are in the process of transitioning from late adolescence to early adulthood, navigating various physical, psychological, and social changes [ 1 ]. After entering the university, undergraduates, especially first-year students, are prone to various maladaptation problems due to changes in their living and learning environments [ 2 ]. Notably, a recent nationwide survey of 48,789 undergraduate students from 31 provinces and cities of China showed that 24.17% of undergraduates were at risk of depression, and 49.58% were at risk of anxiety [ 3 ].

Some studies have shown that these psychological problems are related to culture. As a Chinese proverb goes, “Nothing is more important than learning.” Before university, Chinese students focused solely on their studies, with their parents managing all aspects of life [ 4 ]. Consequently, they may lack the ability to independently resolve problems, particularly when confronted with many challenges in university life, often feeling helpless. Furthermore, admission to university is considered an honor to ancestors and a source of pride for parents in Chinese culture [ 5 ]. Attaining good grades and securing an ideal career post-graduation are seen as ways for college students to fulfill their filial duties, like supporting their parents, thus imposing familial and communal pressures.

Cultural influences also play a role in the mental health of college students. Wang et al. (2016) investigated how traditional Chinese philosophies—such as relationship harmony (advocated by Confucianism), dialectical coping (from Taoism), and non-attachment (rooted in Buddhism)—impact college students’ mental health. Their research demonstrated these philosophies’ negative correlation with psychological distress and negative emotions while displaying positive correlations with self-esteem, positive emotions, meaning of life, and happiness [ 6 ]. Another study indicated that Chinese college students scoring higher in Zhongyong thinking exhibit lower anxiety and depressive symptoms, along with higher self-esteem and life satisfaction, versus those with lower scores [ 7 ].

Since culture and mental health are mutually embedded [ 8 ], different cultures may interpret the same things differently. For instance, in Western cultures, pursuing a college education is often viewed as an individual pursuit, whereas in collectivist China, but in China, higher education is commonly sought to elevate social status and offer enhanced financial support to parents, such as securing a comfortable retirement home. In times of conflict, individuals in Chinese society tend to adopt the principle of “taking a step back and yielding vastness and spaciousness to others” [ 9 ], prioritizing long-term harmony over immediate gains by favoring conflict avoidance over confrontation. The values of “harmony is precious” and the practice of “forbearance” are revered in China, whereas in Western societies, it may be considered unhealthy, with individuals opting for direct expression of discontent [ 10 ].

In China, only 8% of the population hold bachelor’s degrees [ 11 ], and college students are seen as the nation’s hope and future [ 12 ], underscoring a heightened focus on their mental health. To enhance the mental health of Chinese college students effectively, it is imperative to grasp the cultural nuances defining mental health across various contexts.

Mental health has always been a focus in the field of psychology. Researchers from diverse backgrounds have extensively investigated mental health within various cultural frameworks. This includes the development of nuanced interpretations and pertinent theories regarding mental health across different cultural settings [ 13 , 14 ]. Moreover, scholars have localized measurement tools through meticulous adaptations [ 1 , 15 , 16 , 17 ] and delved into understanding the impact mechanisms between mental health and its associated determinants [ 18 , 19 ]. In terms of the connotation of mental health, aside from the various approaches of counseling and psychotherapy have their interpretations and definitions of mental health, various organizations and scholars have also put forward different perspectives of mental health from multifaceted viewpoints, clearly demonstrating the impact of culture.

According to the Concise Encyclopaedia Britannica, mental health is defined as “the state of optimal functioning of the individual psyche within the limits of its own and environmental conditions, but not as an absolute state of perfection” [ 20 ]. Meeks and Heit describe mental health as “the ability to perceive and express one’s emotions and state of mind; mental health is the ability to accept reality as it is” [ 21 ]. Meanwhile, Ryan and Deci propose that mental health involves “the ability to feel effective and agile, e.g., to have full self-fulfillment” [ 22 ]. The World Health Organization defines mental health as “a state of well-being in which the individual realizes his or her abilities, can cope with the normal stresses of life, can work productively and fruitfully, and can make a contribution to his or her community” [ 23 ]. These definitions illustrate how Western culture emphasizes individual capabilities, states of being, and overall well-being, focusing on fulfilling potential, fostering self-esteem, and reflecting a culture centered on the individual.

In the Dictionary of Psychology (Chinese version), mental health was defined as “a good state in which the individual’s mental state (e.g., general adaptability, soundness of personality) remains normal or at a good level, and in which harmony is maintained within the self (e.g., self-awareness, self-control, self-experience) and between the self and the environment” [ 24 ]. According to Zhang and Yang, mental health contains objective and subjective components [ 25 ]. An individual’s mental health is mainly expressed by the relationship between the individual and others in a group, so it contains social meaning. Hu suggests that mental health is about “following one’s heart and not exceeding the rules,” which has both its individual (developmental and autonomous) and social (adaptive and normative) aspects [ 26 ]. Yip defines mental health as a direction that suggests self-discipline and obedience to social order to maintain inner balance and external harmony with others [ 27 ]. Specifically, individuals can maintain this balance and harmony across three levels: personal, interpersonal, and moral/ethical. These definitions underscore Chinese scholars’ emphasis on the social aspects of the individual in conjunction with the proper functioning of mental faculties. They highlight Chinese culture’s focus on harmony, interpersonal relationships, societal connections, and moral/ethical considerations.

In summary, concepts and understandings of mental health are closely tied to culture [ 28 ], reflecting that the connotations of mental health defined by different cultural contexts can vary to some extent. Then, how is mental health related to culture? The theory of sociocultural models (TSCM) provides a perspective on the interaction between culture and the individual mind [ 29 ].

The primary thesis of the theory of sociocultural models (TSCM) is that the human mind and culture mutually constitute each other. During continued interactions, individuals internalize the social culture into their psychological realities to regulate their actions and interactions. Conversely, community members will externalize the psychological reality through enactment and instantiation, creating new social cultures through social interactions and co-construction with the existing social culture. The dialectical interactions of these two aspects constitute the mechanism of the sociocultural regulation of human actions and the construction of the sociocultural reality [ 29 ]. Consequently, social culture dictates varying expectations for mental health standards, while the characteristics associated with mental health are also culturally rooted and reflect social culture. Simultaneously, societal depictions of mentally healthy individuals contribute to the evolution of novel cultural norms in a reciprocal manner.

The Chinese culture has a long history of rich mental health concepts deeply rooted in philosophies such as Confucianism, Buddhism, and Taoism. Confucianism seeks to go into the society( Rushi ), i.e., “To ordain conscience for Heaven and Earth, to secure life and fortune for the populace, to carry on lost teachings of ancient sages, to build peace for posterity” (Zhang Zai: Heng Qu Yi Shuo ). When encountering setbacks, Confucianism advocates being adaptable to circumstances and maintaining mental health by being resilient and motivated. Taoism seeks to transcend the world( Chaoshi ) and advocates “letting go.”When encountering difficulties, people maintain mental health by going with the flow and doing what they should do. The philosophy also underscores the importance of balancing Yin and Yang, enabling individuals to perceive challenges holistically by acknowledging both positive and negative aspects. Buddhism seeks to jump out of the material world( Chushi ) and advocate “being free of worried thoughts” when encountering difficulties. As Hui Neng(the Sixth Patriarch of Zen) said in the Tan Jing, “Since everything is naught, where can dust gather?” Individuals can cope better with difficulties if they have a mindset that looks down on gains and losses and that everything is nothingness.

Popular anecdotes and proverbs in Chinese culture also dictate criteria for individuals’ mental health. For instance, the “Three Feet of Space” tale narrates an incident from ancient China where the Guo family faced a boundary dispute with their neighbor during house construction. Upon hearing of this issue, patriarch Guo Pu wisely proposed, “Sending letters a thousand miles just for a wall; why not give him three feet?” This led to the Guo family’s compromise, and finally, both families conceded three feet of space from their walls. This narrative underscores the cultural emphasis on fostering interpersonal harmony through mutual accommodation, viewing discordant relationships as signs of poor mental health.

Contemporary scholars have also endeavored to directly integrate key concepts from Chinese traditional culture into psychological counseling and therapy. Yang and his colleagues(2002) [ 30 ] created Taoist Cognitive Therapy to facilitate cognitive restructuring in psychologically distressed individuals by directly applying the 32 characteristics of the Taoist principle of health, that is: “Benefit without harm, but not disputing; abstinent contentment with little selfishness and desire; under the knowledge and the place, let gentleness overcome rigidity; recover the original simplicity, let it be.” Liu(2023) posits that “unity of universe and human” in Chinese culture is a core idea of mental health [ 31 ]. He pointed out that the psychological phenomenon corresponding to this concept is psychological nothingness. By fusing modern psychotherapy with the concept of “unity of universe and human,” Liu developed the technique of “Moving symptom’s symbol to nothingness” to fulfill the healing role of Chinese culture. These endeavors establish a robust framework for comprehending mental health through the lens of Chinese cultural perspectives.

Over the years, numerous scholars have delved into the attributes of mentally healthy college students. Prominent among these is Wang and Zhang’s widely recognized framework, which outlines eight characteristics drawing from personal experience: understanding and accepting oneself; accepting others and dealing well with them; facing reality squarely and accepting it; loving life and enjoying work; being able to coordinate and control emotions and being in a good state of mind; having a complete and harmonious personality; having normal intelligence; and having age-appropriate mental behavior [ 32 ]. However, this work has predominantly focused on psychological cognition, emotion, and intention, with limited consideration of the cultural context, particularly the influence of Chinese culture on mental health.

Subsequently, scholars such as Zeng and Lei, incorporating social, ethical, and moral perspectives, proposed a culturally nuanced framework emphasizing four main traits in mentally healthy college students: positive and controllable emotions, good moral values, comfortable coping with schoolwork, and healthy social interaction [ 33 ]. While valuable, this perspective primarily mirrors researchers’ subjective experiences and authority-driven viewpoints. It neglects insights from laypeople, omits identification of the aspects of Chinese culture showcasing characteristics of mentally healthy college students, and lacks differentiation between mentally healthy college students and other demographic groups. Consequently, there is a demand for exploring innovative methodologies to scrutinize the attributes of mentally healthy college students, particularly focusing on characteristics within Chinese culture.

Currently, there are various research paradigms for the study of mental health. Jiang (2004) categorized them and concluded that there are two main principles in evaluating mental health: the majority principle and the elite principle [ 34 ]. The majority principle refers to a research paradigm that selects research subjects through large samples and measures whether individuals deviate from the norm through the principle of statistical normal distribution [ 35 ]. An example is applying the Chinese version of Symptom Checklist-90 (SCL-90), one of the most often used self-report symptom inventories to measure the mental health of college students, and individuals scoring exceeding the norm were considered abnormal [ 36 ].

The elite principle refers to a research paradigm that focuses on elite samples, namely a small number of relatively outstanding individuals in the whole population who are at the tip of one side of the normal distribution, and primarily employs qualitative research methods to derive research findings [ 35 ]. For example, Maslow researched some great people in Western history( i.e., self-actualized people) using qualitative research methods such as biographical analysis, depicted 15 characteristics of self-actualized people, that is, “more efficient perception of reality and more comfortable relations with it,” “acceptance (self, others, nature),” “spontaneity; simplicity; naturalness,” “problem centering,” “the quality of detachment; the need for privacy,” “continued freshness of appreciation,” “autonomy; independence of culture and environment; will; active agents,” “the mystic experience: the peak experience,” “gemeinschaftsgefuhl,” “interpersonal relations,” “the democratic character structure,” “discrimination between means and ends, between good and evil,” “philosophical, unhostile sense of humor,” “creativeness,” “resistance to enculturation; the transcendence of any particular culture” [ 37 ].

Maslow’s findings profoundly influenced research on mental health definitions, standards, and interventions. While some researchers have embraced the characteristics of self-actualized people as an ideal standard of mental health [ 38 ], others have leveraged these characteristics by focusing on exceptional psychological qualities rather than normative behavioral performance [ 39 ], and many of these characteristics have been used as ideal indicators of mental health for the promotion of mental health among college students [ 40 ]. Additionally, these characteristics and the conditions that promote or inhibit self-actualization are also applied in methods and paths of healthy human development [ 41 ]. Furthermore, specific characteristics such as a “philosophical, unhostile sense of humor” have been directly applied by researchers to enhance humor quality among college students facing stress and embarrassment, aiming to uphold their mental well-being [ 42 ].

Despite significant value in both theory and practice, Maslow’s study is based on the Western culture and is not aimed at a specific group of college students. Consequently, its direct relevance to enhancing the mental well-being of Chinese college students may be limited, necessitating further investigation into mental health within the framework of Chinese culture. Nonetheless, Maslow’s study of the elite samples of self-actualized people also provides a new research paradigm for mental health research, which has greatly inspired this study.

In the past, most studies on the mental health of college students used quantitative studies based on the majority principle. While some qualitative studies inquiries delved into the characteristics of mentally healthy college students, these studies often focused on specific subgroups like those who experienced being left behind [ 43 ] or childhood trauma [ 44 ]. A gap exists in the mental health characteristics based on the Chinese culture of college students who are the elite samples, i.e., those who exhibit very good mental health. By utilizing the elite principle paradigm, researchers can gain insights into and depict the mental health characteristics of college students within the context of Chinese culture, with the ultimate aim of delineating the mental health characteristics of college students specific to this cultural framework.

This study will apply the elite principle to examine college students with very good mental health. Through a distinctly Chinese cultural lens, this research aims to delineate what mentally healthy college students look like and what characteristics they show. By focusing on college students’ personal experiences and Chinese culture, this study positions college students as knowledge generators, employing a qualitative research approach to uncover the characteristics of mentally healthy college students. The objective is to achieve a new understanding of college students’ mental health based on Chinese culture and provide a theoretical basis for new mental health standards and a reference for promoting, cultivating, and intervening in college students’ mental health.

In this study, mental health refers to the good psychological state of an individual. College students refer to the group of students who are receiving professional higher education. Chinese culture refers to the culture created by the Chinese over thousands of years of development, from ancient times to the present [ 45 ].

The study applied a participatory, exploratory, qualitative design. Qualitative methods are suitable for exploring the meaning of phenomena or life events to the interviewees and their inherent experiences from the subjectivity of the interviewees [ 46 ]. It also emphasizes the participants as a generator of knowledge and the acquisition of significant experiences from the participants [ 47 ]. Thus, it can help researchers to gain a deeper understanding of community members in a specific cultural-historical context. Moreover, qualitative methods hold particular promise for prioritizing participants’ voices, and they contribute to understanding human interaction with the environment in development and helping researchers build and expand new concepts and theories in specific cultural-historical contexts [ 48 ]. This study used semi-structured individual in-depth interviews to explore the characteristics of mentally healthy college students based on Chinese culture. Moreover, the procedure of the study is shown in Fig.  1 .

The procedure of the study

The development of the interview outline

The qualitative data for this study was collected through semi-structured interviews. Interviews serve as a tool to help reveal and understand participants’ perspectives and experiences. The interview outline for this study was based on the theory of sociocultural models [ 29 ], focusing on how the interviewed college students understood Chinese culture and which cultures were internalized as characteristics of mentally healthy college students.

The interview outline in the pre-interview includes questions such as “What do you think is mental health? What do you think a ‘mentally healthy’ college student is like? You can use yourself or your classmates as examples.” “What do you think is Chinese culture? What is your understanding of Chinese culture?” “What do you think is related to college students’ mental health in Chinese culture?” (Appendix 1 ).

Participant recruitment and selection

The selection criteria for the participants were: i) undergraduate students enrolled in colleges; ii) having a very good psychological status, with a self-assessment of mental health of 7 or more (out of 10); and iii) self-assessment anxiety/depression scores within the normal range.

The study recruited participants through postings in contact groups and forums among different colleges. Undergraduates who satisfied the selection criteria volunteered to participate in the study. At the time of self-referral, enrolled students rated their mental health with the term “Out of ten, how would you rate your mental health?” as well as filled out self-rated anxiety and depression scales [ 49 , 50 ].

The reasons for considering selection criteria are as follows. Firstly, the research objective is to identify the mental health characteristics of college students with good mental health. Therefore, following the elite principle and referencing Maslow’s self-actualization research paradigm [ 37 ], we have chosen exceptionally mentally healthy college students as elite samples for study. Given that statistical analysis commonly regards the top 27% as the criterion for high-score groups [ 51 , 52 ], a score of 7 out of 10 indicates high mental health levels. Consequently, the study interviewed college students scoring at least 7 points. Secondly, to eliminate individuals with significant biases in the self-assessment of mental health and those potentially experiencing psychological issues, we utilized scores from self-rating scales for depression and anxiety to exclude possible candidates with underlying psychological disorders.

Eventually, 17 college students who met the criteria were selected for interviews in this study. The selection of participants considered factors that might influence college students to develop different understandings of Chinese cultures, such as upbringing, family environment, and educational experiences. The total number of participants was determined based on thematic saturation, i.e., no significant themes emerged with new respondents [ 53 , 54 ]. Finally, 17 undergraduate students volunteered to participate in the formal interviews, and the self-reported mental health score of the interviewees was 8.11(SD = 0.90) (out of 10). Among the participants, seven were male, and ten were female. Their ages ranged from 19 to 23 years old (mean age = 20.82, SD = 1.33 years), five interviewees were from Double World-Class Project Universities in China, and 5 were first-year students, two sophomores, eight juniors, and two seniors. Participants came from different regions of China; 4 grew up in north China, 1 in northwest China, 2 in southwest China, 2 in south China, 1 in east China, and 7 in central China; 1 from an ethnic minority. 65% were from urban areas, and 29% had no siblings. Additional information on parents’ education level and occupation is shown in Table  1 .

After the interviews, participants were thanked for their participation and contribution and were offered 30 RMB (about 4 dollars) for participating.

The finalization of the interview outline

Before the formal interviews, three college students (one male and two female) who met the selection criteria were pre-interviewed, and the interview outline was adjusted based on the pre-interviews. Specifically, the researchers adjusted ambiguous expressions. For example, in the pre-interview, the researchers found that if they asked the interviewees, “What do you think is related to college students’ mental health in Chinese culture?” They answered how Chinese culture affects college students’ mental health rather than the characteristics of mentally healthy college students based on Chinese culture. Therefore, we adjusted the question to “What a ‘mentally healthy’ college student is like based on Chinese culture? You can take yourself or your classmates as an example” to obtain the characteristics of mental health that reflect Chinese culture. A formal interview outline was eventually formed (Appendix 2 ).

Data collection and analysis

The qualitative data was collected through in-depth personal interviews with eligible college students. Each interview lasted between 50- 100 min and was conducted by the researcher (MG), who possessed a doctoral background in psychology, had received training in qualitative research methods, and had three years of experience working in mental health education in universities. All participants signed informed consent forms prior to the interviews. In total, 1252 min of interviews were conducted with 17 participants, which were then manually transcribed by MG, resulting in 289,000 words of interview transcripts.

To accurately ascertain the true meaning expressed by the participants, this study employed manual analysis within the research team to code and analyze the interview transcripts word by word and sentence by sentence. Under the guidance of XJ (a clinical and counseling psychology professor), the research team completed all data analysis work. In addition to MG and WW, the team members included two doctoral students who are also full-time university psychological counselors and two master’s students specializing in mental health education.

The data analysis was conducted using thematic analysis [ 55 ]. The steps are as follows: first, the researcher transcribed each of the digitally recorded interviews, immersed within the data, and repeatedly read through the 289,000-word interview transcripts. Second, researchers identified meaningful texts and created open codes. Each meaningful sentence was marked with a “code number,” totaling 1,889. The study used “F” to represent female interviewees and “M” for male participants. The first number represents the interview orders of interviewees; the second number represents the order of the meaningful statements in the interview. For example, “M5-40” represents the 40th word, sentence, or paragraph spoken by the fifth male interviewee. Third, after contemplating the open codes repeatedly, 168 manifestations of mentally healthy college students were derived through initial coding. These manifestations were then summarized to establish 18 characteristics of psychologically healthy university students via focused coding. Subsequently, these 18 characteristics were further classified through core coding to derive five main themes. Fourth, we checked the themes and adjusted their structure until they met internal homogeneity and external heterogeneity criteria. Fifth, we defined and named the themes; 18 characteristics were obtained and coded into five themes.

The Delphi expert evaluation

Subsequently, three experts were invited to assess the appropriateness of naming, defining, and classifying the identified 18 characteristics and five themes above. These experts are professors in clinical and counseling psychology from institutions such as Beijing University of Chinese Medicine, with in-depth research in Chinese culture and mental health. They have published numerous related monographs and academic papers, such as “When Psychological Counseling Meets Traditional Culture” and “Mind Operations in Meditation.”

The evaluation comprised two rounds. The first round involved a focused group interview where the three experts individually reviewed each theme, characteristic, and original interview data, offering suggestions for revision. They generally approved of the theme divisions and most characteristics, with two main modifications: 1) the integration and categorization of specific characteristics, such as the initial characteristic “Having a pleasant disposition,” which was deemed by experts to contribute to a comfortable interpersonal state and thus was incorporated into “Interpersonal harmony and comfort.” 2) Adjustments to specific nomenclature, such as refining “Showing filial respect to parents” to “Showing filial respect to parents appropriately” to better emphasize the nuance of the characteristic.

The revised results were resent to the three experts for a second round of evaluation, leading to a consensus with no further modifications suggested, thus finalizing the research findings.

The trustworthiness of the data

Trustworthiness was achieved in several ways.

First, to minimize personal biases to the greatest extent possible, the researchers continuously reflect at each stage of the research project, remaining attentive to the influence of their own experiences and biases throughout all research and analysis phases. For instance, MG utilized a reflective journal [ 56 ] to document personal perspectives after each interview, consistently reminding herself to avoid preconceived notions.

Second, the selection of participants considered factors that might influence college students to develop different understandings of Chinese cultures to ensure the diversity of the participants. And, the total number of participants was determined based on thematic saturation [ 53 , 54 ]. In this study, after interviewing the F8(the 14th interviewee), no new significant themes emerged. Then, three more interviews were conducted (F9, F10, M7), and no significant themes emerged with the new respondents.

Third, the research performed investigator triangulation [ 57 ]. Independent researchers completed comparative analyses of individual findings, organized regular research team meetings to compare the analyses, and identified relevant themes. Moreover, XJ frequently reviewed interviews conducted by MG, her reactions to interviews, and the formulation of results. All the researchers discussed the coding and the corresponding original text until a consensus was obtained to bolster the study’s credibility and dependability.

Fourth, external audits are conducted to foster the accuracy or validity of a research study [ 57 ]. The research invited three experts above who have made achievements in Chinese culture and mental health to assess the appropriateness of naming, defining, and classifying the characteristics and themes in order to enhance the reliability of research findings.

College students’ understanding of Chinese culture

The interviewees’ understanding of Chinese culture was focused on the following four main aspects, and the participant’s identifier follows quotations.

Firstly, Chinese culture is undoubtedly distinct from other countries. For example, F1 believes that “Chinese culture is not just some fixed dynasties in history, or language, or what some literati or educators or some people said, it refers to some patterns of behavior or some ideas that distinguish people from other countries” (F1-66) and is unique to China (M3-110).

Secondly, Chinese culture includes both traditional and modern Chinese new cultures (e.g., revolutionary spirit, M2-95, M4-151, M7-85). Moreover, it is argued that Chinese culture is the essence of what has been left behind through history, including all aspects that have been handed down from ancient times to the present (M1-99; M5-128), and that it is a continuous transmission (F2-72, F4-92, F5-170, F7-137; F9-181; M6-132) and a fusion of the old and the new (F7-142). Chinese culture is implicitly formed and constantly influences and permeates everyone or their lives (F3-134; F7-138; M1-102; M3-111).

Thirdly, Chinese culture is a macro concept, encompassing both intangible and physical aspects. Intangible aspects include thoughts, spirits, and qualities (M2-95, M4-151, M5-131). The physical component includes not only literary works such as poetry (as perceived by all respondents) but also Chinese language and writing (Chinese characters, F2-75; oracle bone inscriptions, F9-184; calligraphy, F2-77, F5-170, F8-94), architecture (F3-148; F10-98), costume (F3-141; F10-101), and folkloric performances (drama, F2 -74; shadow puppets, F5-168; martial arts, F7-141), gastronomy (M5-132), art (painting, F8-93; paper-cutting, M1-100, M2-96, F5-169; china, M2-97; F2-75), traditional festivals and customs (M3-107; F3-138; F5-166; F7-140; F0-97. M7-87) and many others.

Fourthly, some important historical and modern figures mainly reflect Chinese culture’s ideological and spiritual aspects. For example, the famous statesman and literary figure Wen Tianxiang of the late Southern Song dynasty, whose poems “Everyone must die; let me but leave a loyal heart shining in the pages of history books” showed the interviewees his righteousness (M4-122), resilient, his moral integrity (F6-77), and his fearlessness in dedicating his life to his country (M2-72). There is also Zhou Enlai’s ambitious pursuit of “Reading for the rise of China” (M4-62), Mao Zedong’s sense of family and country and the importance he attached to learning (M5-43), and Qian Xuesen’s strict demands on himself during his research (M4-126). The interviewees also made many references to literary figures, such as Li Bai, a poet of the Tang dynasty, whom several interviewees mentioned for his free and ease in the face of frustration (M2-92, M6-30), and his ability to show his spontaneous side in life and learn things spontaneously(M5-54). As well as the ambition of Du Fu showed in his poem “When you are standing on the peak, you are on top of the world” (M5-36), and his sense of responsibility (M1-91, F3-56) reflected in his other poem, “To Emperor Yao and Shun, and to make the customs simple again” (M1-91, F3-56). They also talked about Su Shi’s open-mindedness (F8-79; M6-9) and cheerfulness (M5-29) in the face of adversity, who is a famous poet, calligrapher, gourmet, and hydraulic expert in the Northern Song Dynasty; and also the inner peace(M6-15) and indifference (F3-53) of Tao Yuanming (a famous idyllic poet in the Eastern Jin Dynasty) from his poem “I pick fence side asters at will; carefree I see the southern hill,” and so on.

In addition, the spirit of Chinese culture is also reflected in some allusions and some historical events in ancient and modern times, for example, “Mencius’ mother moves her home three times to better her son’s education” (F1-60), “Che Yin makes use of the light of fireflies or the reflected light by the snow to study” and “Kuang Heng dug a small hole on the wall in order to get some light from the neighbor’s house to read books” (F1-61; F8-34). These allusions convey the importance of studying hard even when conditions are limited. Also, the revolutionary spirit of the May Fourth Movement shows that young people are not afraid of sacrifice (M4-29), and the New Democratic and Industrial Revolution embodied the unity of the Chinese people (M7-91).

Characteristics of mentally healthy college students based on Chinese culture

There are eighteen characteristics of mentally healthy college students based on participants’ understanding of Chinese culture as described above, which is coded into five core themes: (1) value pursuit, (2) life attitude, (3) interpersonal ideal, (4) behavior ability, and (5) self-cultivation. It can be seen that the vast majority of the mental health characteristics reflect traditional Chinese culture, which is constantly being passed down and changed, with the remainder reflecting the influence of modern Chinese culture. The five themes and corresponding characteristics are shown in Table  2 . The results are presented below, and the participant’s identifier follows quotations.

Value pursuit

Value pursuit refers to an individual’s understanding and practice of life ideals and beliefs after integrating social consciousness, such as worldview, life view, and values. Participants described that mentally healthy college students based on Chinese culture have strong beliefs and goal pursuits of contributing to the motherland. They exhibit profound loyalty towards their motherland, viewing its service as their sacred duty, and are steadfast in their resolve to contribute through bold exploration, even in the face of daunting challenges or the prospect of personal sacrifice. This theme directly reflects the Chinese Confucian culture of “Self-cultivation is the starting point of several steps moving outward. The next step is managing family affairs, followed by governing the state. The final step is moving to provide peace and sound governance to all under heaven” and “To be the first in the country to worry about the affairs of the state and the last to enjoy oneself.” The following three subthemes were identified regarding students’ value pursuit.

(1) Loving their motherland and identifying with their culture

First and foremost, mentally healthy college students love their country and are firmly convinced that they want to identify with it. Twelve interviewees emphasize that mentally healthy college students should embody love for their country, cultural identification, and a profound sense of belonging and national pride. On the one hand, they are patriotic and loyal to their motherland and have high moral characters. For example, one participant said, “ like the patriotism in Yue Fei (a famous military man, strategist, calligrapher, poet, and national hero in Chinese history, and was the first of the Four generals rebuilding the Song dynasty). His patriotism and loyalty are also what a mentally healthy college student should have ” (#M6-54).

On the other hand, they identify with the country, nation, and culture from the heart and are proud of the motherland. Another participant said, “ Mentally healthy college students should have a real sense of cultural identity. Furthermore, a Chinese should identify with the traditional Chinese culture …… ” (#F3-110).

(2) Having a sense of responsibility and mission and being willing to dedicate oneself to the country at any time

In addition, mentally healthy college students have a firm sense of mission and responsibility to the motherland. Ten interviewees assert that mentally healthy college students should exhibit a sense of national responsibility, ambitious aspirations, and a readiness to devote themselves to their homeland wholeheartedly. Mentally healthy college students should have ambitious ambitions. As M1-75 said: “ ‘To ordain conscience for Heaven and Earth, to secure life and fortune for the populace, to carry on lost teachings of ancient sages, to build peace for posterity’ (Zhang Zai: Heng Qu Yi Shuo), which can also reflect the looks of a mentally healthy college student. ”

The most important thing is to be willing to contribute to their motherland, even at the expense of oneself. Another participant said, “ Mentally healthy college students do not think about personal gains and losses too much but put their country and nation before themselves, ……, ‘Death is not my concern should it benefit the country. How can I pick and choose for my loss or gains?’ (Lin Zexu) …… ” (#M7-22).

(3) Daring to criticize, explore, and innovate

At the same time, mentally healthy college students have the quest and conviction to keep climbing to the top. Sixteen interviewees believe that mentally healthy college students are enterprising, daring to criticize, explore, and innovate to contribute to their country’s development. Mentally healthy college students are active, enterprising, and have goals and plans. One participant said, “ I think mentally healthy college students should have goals and plans for themselves ” (#M6-3). They also have critical thinking and exploratory spirit and will keep innovating. As F7 said, “ If you are a mentally healthy college student, you also need some innovative spirit to break through …… ” (#F7-59). Also, they are willing to explore and contribute to the country’s development, as M4 said: “ Mentally healthy college students should be like Qian Xuesen (also known as Tsien Hsue-she), who has a strong spirit of patriotism. He devoted himself to scientific research, and after countless attempts and explorations, he finally launched the first atomic bomb for China …… ” (#M4-124).

Life Attitude

Life attitude is an individual’s understanding and reaction to things that happen in daily life. Participants highlighted that maintaining a positive, optimistic, dialectical, and open-minded stance towards setbacks and challenges is a key characteristic of mentally healthy college students. This theme directly reflects Chinese culture: “Someday, with my sail piercing the clouds, I will mount the wind, break the waves, and traverse the vast, rolling sea.” and “It is blessed to suffer losses.” The following four subthemes regarding students’ life attitudes were identified.

(1) Loving life and being positive

Mentally healthy college students hold positive attitudes about life. Fourteen interviewees believe that mentally healthy college students exhibit optimistic attitudes toward life. Mentally healthy college students approach life optimistically, viewing it as brimming with hope. As F9 mentioned, “ I think I am mentally healthy because I am quite positive and optimistic about life, and I will face it positively even if there are some bad things ” (#F9-149). Moreover, they love life and experience life from their heart, “ I think mentally healthy college students can live a good life. Particularly, they can still maintain a love for life, have something they want to do, have the energy to fight or to live. ” (#M2-2). They always think life is full of meaning. As F1 said, “ I think some of the cases (of mental ill health) are because they have lost hope in life and do not want to do anything ” (#F1-47).

(2) Staying positive and having the ability and quality to cope with hardships

Mentally healthy college students possess a positive attitude towards suffering and setbacks. All interviewees believe that mentally healthy college students have a positive view and the qualities of coping with suffering when facing life difficulties. They will not shy away from adversity; instead, they proactively address issues, surmount obstacles, and manage them with composure. When facing difficulties or setbacks, mentally healthy college students maintain constructive beliefs. As one participant said: “ ‘Just as heaven keeps moving forward vigorously, a man of virtue should strive continuously to strengthen himself’ (The Change of Book). And ‘When Heaven intends to confer a great responsibility upon a person, it first visits his mind and will with suffering, toils his sinews and bones, subjects his body to hunger, exposes him to poverty and confounds his projects. Through this, his mind is stimulated, his nature strengthened, and his inadequacies repaired’ (Mencius). A mentally healthy college student should be like as described in these statements. ” (#F9-25).

They also exhibit the qualities to cope with hardships, such as striving continuously to strengthen themselves, being indomitable, resilient, enterprising, and so on. “ I think indomitable also reflects the self-control mentioned earlier, that is, they will not give up even after experiencing more difficulties ” (#M4-136).

Furthermore, they can analyze and resolve problems amid adversity and challenges, effectively overcoming them. “ For a long time, when my friends and I encounter setbacks, crises, or challenges, I always use this phrase to encourage myself and others to handle it calmly, ‘to be unchanged in front of the collapse of the mountain Tai, and to face danger without being surprised when it suddenly comes in front of you.’ ” (#M7-6).

(3) Being flexible and dialectical

Mentally healthy college students have a dialectical attitude towards life. Ten interviewees noted that mentally healthy college students demonstrate critical thinking skills by approaching situations objectively, comprehensively, and dialectically. These dialectical concepts, attitudes, and behaviors when facing negative things in life are also characteristics of mentally healthy college students. One participant said, “ Mentally healthy college students should be as objective and comprehensive as possible when dealing with things ” (#F3-118). They do not dwell on the present and have a positive attitude toward the future, “ There are plenty of fish in the sea. Do not miss the whole forest because of a tree. Even if you are sad about a breakup, do not cling to the past, but try to live a new life ” (#M7-12).

Furthermore, they think dialectically and believe that all sufferings have its reward. As F1 said: “ A saying goes that ‘Someday This Pain Will Be Useful to You,’ which means that it is not always bad to suffer Loss; think long term. For example, one may sometimes feel that their interests are being threatened in interpersonal relationships. However, if they are particularly concerned about this, it will make them uncomfortable, while if they are generous or forgiving, their heart will become more open ” (#F1-24).

(4) Being inclusive and broad-minded

Mentally healthy college students have an open-minded attitude toward life. Sixteen interviewees believe mentally healthy college students are tolerant, broad-minded, and open-minded. Both for themselves and others, mentally healthy college students hold tolerant attitudes. A participant said, “ I may lack a little tolerance for others because I am always strict with myself, so I may sometimes be strict with others. So, from this point of view, I think my mental health level needs to be further improved ” (#M2-79). They are broad-minded (“ Be magnanimous, as the saying goes, ‘A prime minister’s mind should be broad enough for poling a boat,’ which is a sign of college students’ mental health, advising people to look at whatever things a little more openly ”, #F6-34).

Moreover, even in the face of life’s misfortunes, they are also very liberal and open-minded, able to accept them openly. As M6 mentioned, “ One should also have positive and healthy perceptions. Su Shi, a famous poet, calligrapher, gourmet, and hydraulic expert in the Northern Song Dynasty, openly accepted the fact that he was deprived of his official position. Instead of being depressed daily, he lived an easy and interesting life, free and relaxed ” (#M6-9).

Interpersonal ideal

Interpersonal ideals refer to the pursuit and aspiration of individuals to achieve the best in interpersonal communication and good relationships. According to these interviewees, the characteristics of mentally healthy college students can be divided into general and specific interpersonal relationships. Regarding general interpersonal relationships, mentally healthy college students are friendly and kind, and their interactions with others are harmonious and comfortable. When navigating specific relationships like those with parents, they are filial but have rational thinking; in terms of friendship and romantic partnerships, they pursue ideal and pure relationships. This theme is a direct reflection of Chinese culture: “benevolence,” “harmony is precious,” “The relations between men of virtue are plain like water,” “filial piety,” and so on. The following four subthemes were identified regarding students’ interpersonal ideals.

1） Being benevolent and kind

Mentally healthy college students are benevolent and kind in their interactions with others. Thirteen interviewees believe mentally healthy college students are kind-hearted, compassionate, sincere, caring, and helping others without discrimination. Mentally healthy college students are benevolent and have compassion for others; as M7 mentioned, “ When I met beggars on the road, …… whether they are pretending or be, I am always willing to give them some money…… ” (#M7-54). They are kind-hearted (“ I think a person should be at least kind-hearted; he may have that kind of empathy inside, have that kind of emotion for either other people or animals, ……, and have a softer heart, which also reflects the mental health of college students ,” #F6-45). They treat people gently and friendly (“ Laozi and Confucius look gentler than others, I feel that this characteristic in them also indicates the mental health of college students ,” #M3-73).

Furthermore, they are helpful and kind to others. As one participant said, “ Imagine this: You’re in a crowd, and a bike tumbles to the ground. Everyone is looking around, unsure of what happened. Now, you’re caught in a bind: Should you lend a hand or stay back to avoid being wrongly accused? Despite the chance of misunderstanding, I feel it’s crucial to step up and help. Ignoring the situation just doesn’t sit right with me—it goes against everything I believe in. ” (#F5-161).

2） Interpersonal harmony and comfort

Mentally healthy college students have a harmonious and comfortable interpersonal state. All interviewees agree that mentally healthy college students exhibit pleasant character and interpersonal adeptness, adhere to fundamental Chinese cultural values, and maintain a more harmonious and comfortable relational environment compared to their peers. Mentally healthy college students experience interpersonal harmony and comfort; one interviewee said, “ A mentally healthy college student has better interpersonal relationships, ……and has a comfortable social state ” (#F1-17). In interpersonal interaction, they prioritize harmony (“ I quite agree with the saying ‘Peace is of paramount importance. Since we are studying together, it is important to take care of each other and try to understand each other ”, #M3-49). Besides, they have good interpersonal interactions (“ ones’ mental health, I think, also shows more in whether they can deal with interpersonal relationships with people around them, …… whatever kind of people may meet, they can deal with the relationship well ”, #F6-9).

Moreover, they appreciate others (“ If other people have gained a certain amount of academic achievement, …… if he is (mentally) healthy, he may be happy for others’ success, achievement ”, #M7-33). Also, they can resolve conflicts or contradictions in interpersonal relationships (“ There is no perfect person; for example, if they cause harm to others, they can recognize their mistakes and apologize timely and honestly ,” #M6-101).

Furthermore, they follow many guidelines to create a harmonious and comfortable interpersonal state. As F3 mentioned, “ I think, when it comes to some unimportant things, it is important not to bother others like that…… one should have the sense of proportion ” (#F3-39).

3） Having a soul mate

Mentally healthy college students seek to have a soul mate in specific friendships or romantic partnerships. Nine interviewees suggest that mentally healthy college students possess the ability and quality to communicate and empathize with others on a deep spiritual level and form corresponding friendships or romantic relationships. Whether in friendship or romantic relationships, mentally healthy college students have the correct attitude toward interaction, as F8 said, “ For example, Zeng Gong and Wang Anshi (both politicians of the Northern Song Dynasty), …… They become good friends for life not based on interests, but on their appreciation of each other, and the same values, which I think mental health of college students should always be ” (#F8-67).

They emphasize the spiritual level of communication more than pursuing each other’s company. They have a more high-quality and pure relationship, in friendship or romantic relationships. As F6 said: “ ‘The friendship of a noble person is as pure as water.’ (Chuang-Tzu). Put simply, relationships should be genuine and straightforward, free from fame-seeking or ulterior motives; Just like the story of Boya and Ziqi, mentally healthy students might find a companion who truly gets them, connecting on a spiritual and empathetic level…… ” (#F6-38). It is the same with romantic relationships, as M6 mentioned, “ When you read the poem of Su Shi, for example, ‘Ten years parted, one living, one dead; Not thinking; Yet never forgetting; A thousand Li from her lonely grave; I have nowhere to tell my grief……’ The affection between him and his wife is so deep that it is enviable ” (#M6-42).

4） Showing filial respect to parents appropriately

Mentally healthy college students have rational conceptions of filial piety towards their parents and appropriate, respectful behavior. Eleven interviewees believe mentally healthy college students are filial and rational in their interactions with their parents. Mentally healthy college students show filial piety to their parents appropriately. On the one hand, they practice filial piety by accompanying their parents, communicating more with them, caring for them, repaying them, and so on. As F5 mentioned, “ ‘Our bodies—to every hair and bit of skin—are received by us from our parents’ (Xiao Jing). Mentally healthy college students are grateful and respectful, often care for their parents, and spend more time with them ” (#F5-109).

On the other hand, they also have rational thinking rather than unprincipled obedience regarding filial piety’s “cognition” aspect. As one participant said, “ Not just any kind of filial piety, that is, you should have your thinking and judgment…… ” (#F3-105). Another participant said, “ Proper filial piety is an aspect of college students’ mental health, not that they are obedient to their parents. When they disagree with parents, they can communicate more with parents and let themselves be understood ” (#M5-102).

Behavior ability

Behavior ability refers to the ability of an individual to behave appropriately. According to these interviewees, mentally healthy college students have a variety of behavioral abilities, such as adapting to different environments, learning well, and regulating their emotions. This theme directly reflects the Chinese culture: “Those who obey heaven survive, and those who defy heaven perish,” “learn without thinking is reckless, think without learning is dangerous,” and “When joy, anger, sorrow, and happiness are not yet expressed as a response to other things, they are in a state of balance. When they are expressed in words and deeds by the rites, harmony is achieved. “The following three subthemes were identified regarding students’ behavior ability.

(1) Adapting to the environment

Mentally healthy college students can adapt to the environment. Seven interviewees believe that mentally healthy college students can adapt to different environments. Adaptability is reflected on the one hand in the interpersonal aspects (“ There is also the adaptation to the university environment. Mentally healthy college students can integrate into groups and clubs, and actively participate in club activities ”, #F2-16). Also, they can adapt to different environments (“ I think social adaptability is quite important…… I went to work part-time this summer, but I feel that I have just been exposed to it ”, #F9-10). Moreover, they also show adaptability to adversity (“ I think mentally healthy college students also can adapt to adversity…… ”, #M5-70).

(2) Studying diligently and learning well

Mentally healthy college students can learn well. Thirteen interviewees suggest that mentally healthy college students exhibit a positive learning attitude, take ownership of their learning, maintain a continuous learning process, and demonstrate good study habits. They learn earnestly and diligently and have good learning attitudes (“ College students with good mental health will keep learning, have the initiative to learn, down-to-earth. Moreover, if they work by fits and starts (Cao Xueqin: The Dream of Red Mansions), there will not be a good result ”, #F5-64).

They also actively take responsibility for learning. As F10 said, “ Responsibility is fundamental. The primary task for students is studying. One should stay in one’s lane ” (#F10-83). Besides, they are good at learning (“ I think Lu Xun, who gave up medicine to pursue literature, …… has a powerful ability to learn ”, #F9-71). In addition, they study diligently and accumulate knowledge. As M2 mentioned, “ Since I have to prepare for the entrance examination, I have to memorize words and take lessons every day. That is, ‘But unless you pile up little steps, you can never journey a thousand li; unless you pile up tiny streams, you can never make a river or a sea.’ (Hsun-Tzu: Encouraging Learning), …… I realized that what I do daily is important ”, #M2-93).

(3) Being emotionally appropriate and can regulate emotions

Mentally healthy college students can regulate and manage their emotions. Nine interviewees posit that mentally healthy students display emotional appropriateness and stability, promptly and effectively managing their emotions. Emotions are often regarded as the signal light of mental health. Thus, mentally healthy college students are emotionally appropriate and relatively stable, “ A mentally healthy college student should be emotionally stable, …… ‘The master was mild, and yet dignified; majestic, and yet not fierce; respectful, and yet easy’ (The Analects). One should have a suitable emotion in which state ” (#F3-78).

Moreover, when encountering adverse events, they have the ability to regulate their emotions. As one participant mentioned, “ A mentally healthy college student can control his emotions and regulate his emotions ” (#F6-1). At the same time, they can adjust themselves in appropriate and healthy ways in time, “ when he meets some bad things, he can just communicate with others, exercise…… instead of drinking or even hurting himself ” (#F8-10).

Self-cultivation

Self-cultivation refers to the inner quality or state an individual constantly improves or achieves through long-term efforts and cultivation. According to the interviewees, mentally healthy college students advocate the continuous improvement of self-cultivation. They try to possess many excellent qualities of Confucianism, Buddhism, and Taoism and perfect them daily by having clear and objective self-knowledge and constantly reflecting on themselves to improve their cultivation. This is a direct reflection of the Chinese culture of “no end to learning” and “Seeing the virtuous and thinking of the wise, seeing the unwise and introspecting”, and so on. The following four subthemes regarding students’ self-cultivation were identified.

(1) Having an objective, positive perception of oneself and can accept one’s mediocrity

The constant improvement of mentally healthy college students’ self-cultivation first requires a clear perception of oneself. Eleven interviewees believe mentally healthy college students have a positive, comprehensive, and clear understanding of themselves. They know their strengths and weaknesses and can accept their mediocre and weak sides, “ For example, an Olympic weightlifter, he can only lift 50 pounds, but he had to go lift 100 pounds…… A mentally healthy person should clearly understand themselves and do according to one’s abilities… ”, #F8-33). They also have a positive view of themselves, “ ‘All things in their being are good for something’ (Li Bai: Invitation to Wine); one should not think too lightly of themselves when disillusioned. They can certainly play their usefulness in life, cannot improperly belittle oneself ” (#F9-35). Furthermore, they can also accept their mediocrity and weakness, “ I think there is also a significant point, which is to accept their mediocrity gradually…… ” (#F1-8).

(2) Being confident and also modest

The constant improvement of mentally healthy college students’ self-cultivation also requires an objective perception of oneself. Thirteen interviewees believe that mentally healthy college students are confident and able to stick to what they believe is correct while also being modest. According to a participant, mentally healthy college students believe in themselves, “ This point of believing in oneself in Qian Xuesen is probably also what a mentally healthy college student should have…… ”, #M4-128). They are assertive and can stand firm on their ideas (“ When faced with two choices, mentally healthy college students listen to others’ opinions and at the same time stick to their ow n,” #F4-77). At the same time, they are also modest (“ A saying goes that, ‘Modesty helps one go forward, whereas conceit makes one lag.’ In my opinion, mentally healthy students may not be so proud of themselves……”, #F5-36). Furthermore, they are not overly confident or modest (“Both confidence and modesty in a mentally healthy college student are appropriate and balanced, that is, I think it is necessary to be confident but also modest……, ” #F7-109).

(3) Focusing on introspection and contemplation to align with the sages

Mentally healthy college students improve themselves through constant introspection. Ten interviewees believe mentally healthy college students focus on introspection and are strict with themselves. They constantly check the gaps to seek progress and expand their horizon. Specifically, mentally healthy college students often reflect on themselves (“ ‘I daily examine myself on three points……’ (The Analects) which I think reflects the mental health of college students, that is, whether you are doing your best in the team…… ”, F2-35). They are also strict with themselves, “ As the sayings go, ‘You cannot expect a better world without cleaning your room first,’ although Du Fu (a famous poet of the Tang Dynasty) is said to be very talented, if one cannot do small things well, like cleaning the house, he can do nothing else well ” (#M5-52).

Moreover, they make constant progress and look to the virtuous, “ ‘When you see a person of virtue and capability, you should think of emulating and equaling the person; when you see a person of low caliber, you should reflect on your weak points’ (The Analects). Mentally healthy people also constantly learn from the strengths of others and reflect on their weaknesses ” (#M2-34).

(4) Possessing good qualities advocated by Confucianism, Buddhism, and Taoism, which coexist harmoniously

The highest level of self-cultivation for mentally healthy college students is to possess many good qualities of Confucianism, Buddhism, and Taoism, which together become the characteristics of mentally healthy college students. Sixteen interviewees suggest that mentally healthy college students exhibit strong moral characteristics and virtues from Confucianism, Buddhism, and Taoism, all coexisting harmoniously. Mentally healthy college students have the excellent qualities of Taoism, such as being calm and bland, indifferent to fame and fortune, and peaceful and happy. As the participants said, “ This sense of ordinariness, which I think may also be a necessity for mental health…… ” (#F7- 34); “ Mentally healthy college students are calm and relaxed, take the rough with the smooth; they have confidence in themselves and take it easy ” (#M7-35).

Moreover, they have the excellent virtues of Confucianism, such as benevolence, righteousness, rites, wisdom, and good faith. As F3 said, “ Mentally healthy college students must be good in these virtues, like ‘loyalty, filial piety, rites, wisdom, good faith, and courage’…… ” (#F3-90). Another participant mentioned, “ After comparing so many fictional characters, it is hard for me to use words to describe him (Qiao Feng), …… very filial and loyal, very righteous, …… doing things very fairly, … … ”, #M6-59).

Besides, they also obtain the main qualities of Buddhism, such as gratitude and kindness (“ ‘Moral character can be built by accumulating goodness’ (Hsun-Tzu: Encouraging Learning). A mentally healthy college student does good deeds, such as attending activities as a volunteer…… ” #F2-30). As F9 said, “ Also, mentally healthy college students often remember others’ kindness and are grateful, and then be nice to others, as the saying goes, ‘You throw a peach to me, I give you a white jade for friendship.’ (The Book of Songs) ”, #F9 -112).

The study identified five themes and 18 characteristics of mentally healthy college students within Chinese culture. These characteristics are deeply rooted in Chinese traditions, highlighting yin-yang balance and moral cultivation. They related closely to college students’ identity, learning stage, and age. Contrasting with characteristics of other cultural backgrounds, they showcase the impact of Chinese culture on college students, validating and expanding the theory of sociocultural models.

Comparison with previous studies

Firstly, compared to existing research on the characteristics of mentally healthy college students, this study presents novel findings and unique insights. Consistent with other related studies rooted in Chinese culture, both this study and previous research accentuate that the characteristics of mentally healthy college students encompass facets such as self-awareness, interpersonal relationships, emotional regulation, and positive learning traits. For instance, Wang (1992) posited that mentally healthy college students exhibit characteristics focusing on self-awareness, interpersonal adeptness, and emotional regulation [ 32 ]. Similarly, Zeng (2021) described the characteristics of mentally healthy college students, highlighting their emotional state, academic performance, and interpersonal skills [ 33 ].

Some characteristics revealed in our study diverge from those proposed in prior research concerning their specific connotations. Taking emotional regulation as an example, the research of Zeng (2021) and Wang (1992) primarily emphasized affirming positive emotions. They depicted mentally healthy college students as “positively emotional and controllable” or “possess the capacity to coordinate and manage emotions effectively, sustaining a positive mood.” In contrast, the characteristic identified in this study of “being emotionally appropriate and can regulate emotions” not only encompasses positive emotions but also includes negative feelings, emphasizing the timely and moderate expression of both. This directly reflects the Confucian concept of “Zhongyong” (doctrine of the mean) in Chinese culture, which advocates for moderation in all things, whether positive or negative. Therefore, it is evident that college students’ mental health is closely intertwined with the concept of moderation. Individuals can achieve mental health in various aspects by expressing emotions moderately, whether positive or negative.

Furthermore, this study has identified characteristics not previously mentioned by Chinese scholars, such as “showing filial respect to parents appropriately.” Filial piety is a unique social behavior within Chinese culture, embodying a comprehensive and intricate ethical framework [ 58 ]. Chinese society dramatically emphasizes family values, where treating parents well and acknowledging their upbringing is paramount. Therefore, if one is not filial, one cannot be said to be mentally healthy. However, with the evolution of societal norms, the essence of filial piety has transformed. Recent research reveals that contemporary society no longer adheres to traditional interpretations of filial piety solely through obedience to parents [ 59 ]. This shift signifies that mentally healthy college students now approach filial piety differently, manifesting altered perspectives, attitudes, and behaviors toward this concept. In ancient China, departing from one’s hometown to pursue education and personal growth was discouraged, as staying by one’s parents’ side was deemed the epitome of filial piety. As Confucius stated, “While the father and mother are living, do not wander afar” ( The Analects ). However, today, individuals are encouraged to venture afar to contribute meaningfully to their country and society [ 60 ]. As a result, modern manifestations of filial piety among mentally healthy college students involve not just reverence, care, and support for their parents but also underscore the significance of preserving autonomy and independence while fulfilling their familial duties.

Secondly, upon comparing our findings with research from other cultural backgrounds, it becomes apparent that our results diverge significantly from those of Western culture but align closely with research outcomes from Africa and Asia.

In the West, the understanding of mental health emphasizes enhancing personal belonging, satisfaction, and well-being, which is very different from Chinese culture, which emphasizes self-sacrifice and self-elimination [ 61 ]. Although this study was conducted in a qualitative study of a group of college students in very good mental health, a research perspective similar to Maslow’s research on self-actualizers, there were significant differences in the specific characteristics of these healthy individuals in different cultures. In particular, this study did not address the characteristics of self-actualizers noted by Maslow, such as “the mystic experience: the peak experience” and “philosophical, unhostile sense of humor,” which emphasize excellent personal features. The characteristics identified from this study emphasize individuals’ relationships with the country and family. Such as “loving their motherland and identifying with their culture,” “having a sense of responsibility and mission and being willing to dedicate oneself to the country at any time,” and “Showing filial respect to parents appropriately.” These characteristics are the direct expression of Chinese culture in terms of devoting oneself to the country and being filial to parents, which were not found in the results of Maslow’s study.

On the other hand, this study aligns more closely with research findings from African and Asian cultural backgrounds. For example, in the view of caregivers in Africa and Asia, mentally healthy individuals are people who contribute to the community and spend an enjoyable time in groups [ 28 ]. Thus, college students with good mental health can meet precise requirements at different levels: the individual and others, the individual and the family, and the individual and the nation, which is more of a relationship-oriented “big self” [ 62 ].

Thirdly, this research’s findings correspond with certain facets of the 24 character strengths and 6 virtues outlined in positive psychology, yet they also reveal disparities in specific aspects.

With the burgeoning of the positive psychology movement, some researchers have suggested that people with good mental health are not articulated merely as the absence of mental illness but as people who possess positive qualities, such as being highly resilient and well-being [ 63 ]. Seligman and colleagues summarized 6 virtues and 24 character strengths contributing to a good life [ 64 ], which have garnered wide attention. A point of convergence is that some positive psychological qualities emphasized by the characteristics identified in this study align with those highlighted in positive psychology. For instance, the characteristic of “being benevolent and kind” identified in this study emphasizes that mentally healthy college students are compassionate and kind. Similarly, one of the 6 virtues in positive psychology is humanity, which also focuses on kindness.

Nonetheless, notable distinctions exist between this study and the character strengths or virtues proposed by positive psychology. Firstly, in terms of the connotation of similar qualities, there are variations between the two. For example, the quality of “modesty” as a traditional Chinese virtue holds different implications than the Western perspective on “humility.” Modesty in Chinese culture carries much richer connotations than in the West, and core characteristics such as being open-minded, down-to-earth, and striving for improvement are unique to Chinese culture [ 65 ]. Additionally, while positive psychology views humility as an important but standalone character strength, this study found that mentally healthy college students are “being confident and also modest,” with modesty and confidence blending and coexisting harmoniously. This aligns with the encouragement of self-esteem, confidence, and self-improvement among the younger generation in China in recent years [ 66 ]. However, Chinese people still highly value modesty as a virtue while simultaneously emphasizing confidence. These seemingly contradictory qualities of confidence and modesty are valued, reflecting the dynamic balance of “yin and yang” in Chinese culture [ 67 ].

More importantly, this study has uncovered additional positive qualities beyond the 24 character strengths, such as “being inclusive and broad-minded”.These qualities carry strong moral attributes; in other words, possessing these moral qualities is essential for mental health. Confucianism emphasizes social morality, self-cultivation, and the development of a gentleman-like sage personality [ 68 ]. Self-cultivation is the basis for the ethical construction of family and society to perfect the ideal personality of governing the state and pacifying the world. The concept of “sageliness within and kingliness without” underscores this philosophy [ 69 ]. The characteristic “being inclusive and broad-minded” implies that mentally healthy college students exhibit tolerant and open-minded attitudes, embracing the principles of “Harmony, but Not Uniformity” and “The sea admits hundreds of rivers for its capacity to hold”(Chinese idioms) when encountering diverse viewpoints or adversity. Therefore, a mentally healthy college student possesses virtues such as tolerance and open-mindedness, showcasing solid moral values. In essence, college students’ mental health is intertwined with their moral attributes. A mentally healthy individual must embody essential moral qualities, which serve as markers of their overall well-being. Acknowledging the significance of moral virtues in defining and nurturing mental health among college students is crucial.

Validation and extension to the theory of sociocultural models

Firstly, this study validates the theory of sociocultural models. On one hand, this study confirms how culture influences individual psychology as proposed in the theory of sociocultural models. In this study, psychological entities represent the characteristics of mentally healthy college students that guide their thoughts, behaviors, and attitudes. According to the findings of this study, Chinese traditional culture plays a significant role in shaping these characteristics. For example, the patriotic sentiments of important historical figures such as Wen Tianxiang and Yue Fei, as well as the thoughts of traditional Chinese culture such as “Death is not my concern should it benefit the country. How can I pick and choose for my loss or gains?” (Lin Zexu: Two poems for family members on the way to the garrison ”) and “To ordain conscience for Heaven and Earth, to secure life and fortune for the populace, to carry on lost teachings of ancient sages, to build peace for posterity’ (Zhang Zai: Heng Qu Yi Shuo ) are internalized in the characteristics of “Having a sense of responsibility and mission and being willing to dedicate oneself to the country at any time.” The country cultivates college students as pillars of talent, and Confucianism teaches “To be the first in the country to worry about the affairs of the state and the last to enjoy oneself.” (Fan Zhongyan: The Yueyang Tower ). Thus, studying is not only for personal development but also for a sense of responsibility and contribution to the country, which arguably demonstrates the mental health characteristics of the specific group of college students with distinct traditional Chinese cultural connotations. Such findings align with the theory of sociocultural models, emphasizing how people internalize societal culture into their psychological entities to regulate their psychological activities.

On the other hand, this study validates how individual psychology externalizes and promotes the generation of new culture as proposed in the theory of sociocultural models. During China’s modernization, people have realized that only by daring to break through the shackles of existing ideas and exploring innovative development opportunities can the country move forward and develop sustainably. Many people have overcome difficulties and carried out the revolution, construction, and innovation in constructing Chinese socialism. Their love for the country and their sense of mission made them always meet the challenges of national reconstruction with high morale and perseverance [ 70 ]. Especially since the reform and opening-up, people’s minds have been fundamentally liberated, and the spring of scientific and technological progress has been ushered in. Their precious spiritual wealth, such as the characteristic of “daring to criticize, explore, and innovate,” has facilitated the development of new cultures like Chinese revolutionary and socialist cultures in modern times. Such findings align with the theory of sociocultural models, highlighting how group members externalize their psychological entities and transform them into new social cultures through social interactions and co-construction with existing social cultures.

Secondly, this study expands the content of the theory of sociocultural models. Due to a lack of specific pathways depicting the interaction between culture and psychology in the theory of sociocultural models, this study found that the significant carriers of interaction between culture and individual psychology are the spiritual world presented by historical and modern figures mentioned by the interviewees, as well as tangible worlds such as Chinese characters, poetry, martial arts, and art. These aspects of Chinese culture are internalized by college students as part of their psychological entities, guiding their words and actions and also shaping their perception of mental health. Conversely, the psychological entities of college students, such as the emergence of new concepts like “daring to criticize, explore, and innovate” in the construction of a new China, are transformed into emerging cultures, such as Chinese socialist culture through the role of figures like Qian Xuesen and stories as carriers.

Strengths, limitations, and future research

This study possesses several strengths. Firstly, it is the first attempt to systematically explore the characteristics of college students’ mental health entirely based on Chinese culture. The 18 identified characteristics directly convey or reflect aspects of Chinese culture, significantly enriching the comprehension of college students’ mental well-being within the context of Chinese culture. Secondly, the study adheres to the elite principle research paradigm by using elite samples as participants. Consequently, the outcomes comprehensively delineate the characteristics of mentally healthy college students possessing an excellent psychological state rooted in Chinese culture. These findings not only provide an ideal model for nurturing mental health among college students but also engender fresh insights into mental well-being, culminating in a novel benchmark for mental health standards. Thirdly, this study delves into the unique characteristics of mentally healthy college students within Chinese culture from the students’ firsthand experiences. In contrast, prior scholars predominantly offered personal opinions on the characteristics of mentally healthy individuals based on their experiences, lacking the direct perspectives of college students.

This study also has some limitations. As a qualitative study, the nature of this research inherently limits the applications of its conclusions. Focused primarily on college students, generalizing the findings to other groups in China (such as civil servants) may be constrained. Moreover, this study exclusively examines Chinese college students without conducting cross-cultural research. The absence of direct comparative studies fails to highlight variations in mentally healthy characteristics across diverse cultures. For instance, the absence of a comparative study between Chinese and students from other cultures (such as American college students) hindered exploration into the distinctive characteristics and differences of mentally healthy college students from varying cultures. Consequently, extrapolating the results of this study to other cultural contexts also has its limitations. Despite some similarities between Chinese culture and certain cultures in Asia and Africa, direct inferences also have significant constraints.

Furthermore, in terms of understanding culture, there is no conclusive definition of what culture is and what Chinese culture is. Scholars have put forward many understandings and definitions of Chinese culture from different perspectives. Understanding and defining Chinese culture are still in the exploratory stage, which challenges this study. The researcher’s understanding and mastery of existing relevant knowledge are somewhat limited regarding the formation of research results and the depth of analysis and discussion.

Future research could consider the following aspects. Firstly, a comparative study of the characteristics of mentally healthy people in different cultural groups can be conducted. Since individualistic/collectivistic cultures influence Americans and Chinese to be more expressive of private selves/collective selves, and religious cultures also influence individual self-esteem [ 71 ] and form religious selves [ 72 ]. Therefore, some comparative studies with students from different cultural backgrounds can be conducted in the future. For example, a comparative study with three groups of college students from the United States, China, and India can be considered to compare whether there are differences in the characteristics of mentally healthy college students from different cultures. Secondly, some quantitative studies can be considered. For example, future research could refine specific characteristics identified in the study, like “being flexible and dialectical,” for more specific operational definitions and develop a scale to measure the mental health of different groups to validate how these characteristics are manifested in university students or other groups so that more further research could be conducted using this new scale, which may help facilitate replication of the findings. Thirdly, based on continuous learning and accumulation of Chinese culture, future research can do in-depth excavation and exploration of the manifestation and nature of these mental health characteristics. For example, future research could select the characteristics reflecting the culture of filial piety or Zhongyong culture and explore how these cultures change and develop into mental health characteristics with the development of science and technology, the change of social structure, and the collision of Chinese and Western cultures, which may also be of great significance.

Practical implications

The Chinese culture has rich treasure resources and cultivated Chinese character traits, characteristics, and lifestyles. The results of this study show that many attitudes, ideas, and behaviors espoused by Chinese culture are manifestations of mental health. In particular, this study found the characteristics of mentally healthy college students based on Chinese culture, which is culturally applicable and more suitable for promoting the mental health of Chinese college students and can provide essential references and bases for mental health education and clinical practice.

On the one hand, this study can provide an overall theoretical framework for developing mental health courses for college students. Mental health courses are the most important and direct form of mental health education for college students in China, and they are also the primary way to improve the psychological quality of college students. The Ministry of Education requires colleges and universities to offer mandatory public courses on mental health for undergraduate students [ 73 ]. However, current mental health courses for Chinese college students rely mainly on Western mental health-related definitions, theories, and techniques for delivery [ 74 , 75 ]. The five themes and 18 characteristics discovered in this study are systematic, providing a comprehensive and systematic theoretical basis for college students’ mental health courses.

In particular, the five themes discovered in this study—values pursuit, life attitude, interpersonal ideals, behavioral ability, and self-cultivation—can be employed as the central pillars for teaching and setting objectives in a college student mental health course rooted in Chinese culture. Furthermore, the 18 identified characteristics can form each lesson’s fundamental content and learning goals, establishing a comprehensive framework. For instance, the characteristics “being confident and also modest” can be one of the key topics under the theme of “self-cultivation.” By comparing Western views of mental health (focused on confidence) with Chinese beliefs (valuing both confidence and modesty) and blending students’ self-awareness with Chinese cultural insights, the course can delve into the importance of confidence and modesty in Chinese culture. Strategies for cultivating these characteristics can be discussed, shedding light on the unique aspects of mental health development among college students within Chinese cultural contexts.

Secondly, this research offers valuable insights for fostering healthy personalities among college students in psychological counseling methods from the perspective of Chinese culture. On the one hand, this study has a guiding significance for setting goals in psychological counseling. Psychological counseling has traditionally emphasized decreasing negative emotions and boosting positive ones. Nevertheless, this study serves as a reminder for counselors to reassess this counseling objective. Throughout the counseling process, counselors should not only focus on diminishing negative emotions but also be wary of potential complications stemming from excessive positive emotions, stressing the importance of a moderate expression of positive and negative emotions.

On the other hand, the discoveries of this study could serve as a wellspring of inspiration for crafting indigenous approaches to psychological counseling. This research reveals that mentally healthy college students possess the characteristic “possessing good qualities advocated by Confucianism, Buddhism, and Taoism coexist harmoniously.” Within Chinese culture, the symbiotic interplay among Confucianism, Buddhism, and Taoism stands out as a cornerstone [ 76 ], where these philosophies coexist compatibly and mutually influence each other in shaping Chinese characters [ 77 ]. Future scholars might devise counseling methodologies rooted in the principle of harmonious coexistence found within Confucianism, Buddhism, and Taoism, potentially empowering individuals to bolster their mental health through these culturally embedded psychological counseling approaches.

This study explores the characteristics of mental health of college students with good psychological states from the perspective of Chinese culture and finds 18 characteristics, based on which five themes are formed: value pursuit, life attitude, interpersonal ideal, behavior ability, and self-cultivation. The 18 characteristics are typical of Chinese culture or its features, focusing on multi-level relationships with others, parents, and the country. They are also typical of Chinese culture with moral attributes, an emphasis on self-cultivation, a balance of Yin and Yang, and the coexistence of Confucianism, Buddhism, and Taoism. These findings help enrich the research on culture and mental health, highlight the Chinese cultural connotations of mental health, and help form an ideal standard of mental health for college students. Findings can serve as a theoretical foundation for improving the mental well-being of Chinese college students, act as a guiding light for enhancing students’ mental health, and be integrated directly into the mental health curriculum as course content. Mental health education activities based on these findings can help promote, maintain, and cultivate college students’ mental health literacy and healthy personalities to fulfill their potential and become the pillars of the nation.

Availability of data and materials

The datasets for this study are not readily available because they consist of interview data, for which confidentiality cannot be safeguarded. Therefore, the data will not be made available. Requests to access the datasets should be directed to XJ, [email protected].

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Acknowledgements

We appreciate Professor Tianjun Liu from Beijing University of Chinese Medicine, Professor Jianjun Zhu, and Professor Ming Li from Beijing Forestry University for their support in assessing the appropriateness of naming, defining, and classifying the 18 characteristics and five themes. We thank our research team and participants who shared their experiences and made this study possible. We thank Dr. Xiaofang Yao at Federation University Australia and Dr. Lixian Tu at Shanghai University of Political Science and Law for their support throughout the English translation.

This study is support by the BIT Research and Innovation Promoting Project (Grant No. 2022YCXY053).

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Mingjia Guo, Xiaoming Jia & Wenqian Wang

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MG conducted the interviews, analyzed the data and wrote the manuscript under the guidance of XJ. XJ formulated this study and contributed to editing of the manuscript and critical revisions. WW assisted with the writing and editing of the final manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

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Guo, M., Jia, X. & Wang, W. How would you describe a mentally healthy college student based on Chinese culture? A qualitative research from the perspective of college students. BMC Psychol 12 , 207 (2024). https://doi.org/10.1186/s40359-024-01689-7

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What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography

• Trisha Greenhalgh   ORCID: orcid.org/0000-0003-2369-8088 1 ,
• Julie L. Darbyshire 1 ,
• Cassie Lee 2 ,
• Emma Ladds 1 &
• Jenny Ceolta-Smith 3  

BMC Medicine volume  22 , Article number:  159 ( 2024 ) Cite this article

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Long covid (post covid-19 condition) is a complex condition with diverse manifestations, uncertain prognosis and wide variation in current approaches to management. There have been calls for formal quality standards to reduce a so-called “postcode lottery” of care. The original aim of this study—to examine the nature of quality in long covid care and reduce unwarranted variation in services—evolved to focus on examining the reasons why standardizing care was so challenging in this condition.

In 2021–2023, we ran a quality improvement collaborative across 10 UK sites. The dataset reported here was mostly but not entirely qualitative. It included data on the origins and current context of each clinic, interviews with staff and patients, and ethnographic observations at 13 clinics (50 consultations) and 45 multidisciplinary team (MDT) meetings (244 patient cases). Data collection and analysis were informed by relevant lenses from clinical care (e.g. evidence-based guidelines), improvement science (e.g. quality improvement cycles) and philosophy of knowledge.

Participating clinics made progress towards standardizing assessment and management in some topics; some variation remained but this could usually be explained. Clinics had different histories and path dependencies, occupied a different place in their healthcare ecosystem and served a varied caseload including a high proportion of patients with comorbidities. A key mechanism for achieving high-quality long covid care was when local MDTs deliberated on unusual, complex or challenging cases for which evidence-based guidelines provided no easy answers. In such cases, collective learning occurred through idiographic (case-based) reasoning , in which practitioners build lessons from the particular to the general. This contrasts with the nomothetic reasoning implicit in evidence-based guidelines, in which reasoning is assumed to go from the general (e.g. findings of clinical trials) to the particular (management of individual patients).

Not all variation in long covid services is unwarranted. Largely because long covid’s manifestations are so varied and comorbidities common, generic “evidence-based” standards require much individual adaptation. In this complex condition, quality improvement resources may be productively spent supporting MDTs to optimise their case-based learning through interdisciplinary discussion. Quality assessment of a long covid service should include review of a sample of individual cases to assess how guidelines have been interpreted and personalized to meet patients’ unique needs.

Study registration

NCT05057260, ISRCTN15022307.

Peer Review reports

The term “long covid” [ 1 ] means prolonged symptoms following SARS-CoV-2 infection not explained by an alternative diagnosis [ 2 ]. It embraces the US term “post-covid conditions” (symptoms beyond 4 weeks) [ 3 ], the UK terms “ongoing symptomatic covid-19” (symptoms lasting 4–12 weeks) and “post covid-19 syndrome” (symptoms beyond 12 weeks) [ 4 ] and the World Health Organization’s “post covid-19 condition” (symptoms occurring beyond 3 months and persisting for at least 2 months) [ 5 ]. Long covid thus defined is extremely common. In UK, for example, 1.8 million of a population of 67 million met the criteria for long covid in early 2023 and 41% of these had been unwell for more than 2 years [ 6 ].

Long covid is characterized by a constellation of symptoms which may include breathlessness, fatigue, muscle and joint pain, chest pain, memory loss and impaired concentration (“brain fog”), sleep disturbance, depression, anxiety, palpitations, dizziness, gastrointestinal problems such as diarrhea, skin rashes and allergy to food or drugs [ 2 ]. These lead to difficulties with essential daily activities such as washing and dressing, impaired exercise tolerance and ability to work, and reduced quality of life [ 2 , 7 , 8 ]. Symptoms typically cluster (e.g. in different patients, long covid may be dominated by fatigue, by breathlessness or by palpitations and dizziness) [ 9 , 10 ]. Long covid may follow a fairly constant course or a relapsing and remitting one, perhaps with specific triggers [ 11 ]. Overlaps between fatigue-dominant subtypes of long covid, myalgic encephalomyelitis and chronic fatigue syndrome have been hypothesized [ 12 ] but at the time of writing remain unproven.

Long covid has been a contested condition from the outset. Whilst long-term sequelae following other coronavirus (SARS and MERS) infections were already well-documented [ 13 ], SARS-CoV-2 was originally thought to cause a short-lived respiratory illness from which the patient either died or recovered [ 14 ]. Some clinicians dismissed protracted or relapsing symptoms as due to anxiety or deconditioning, especially if the patient had not had laboratory-confirmed covid-19. People with long covid got together in online groups and shared accounts of their symptoms and experiences of such “gaslighting” in their healthcare encounters [ 15 , 16 ]. Some groups conducted surveys on their members, documenting the wide range of symptoms listed in the previous paragraph and showing that whilst long covid is more commonly a sequel to severe acute covid-19, it can (rarely) follow a mild or even asymptomatic acute infection [ 17 ].

Early publications on long covid depicted a post-pneumonia syndrome which primarily affected patients who had been hospitalized (and sometimes ventilated) [ 18 , 19 ]. Later, covid-19 was recognized to be a multi-organ inflammatory condition (the pneumonia, for example, was reclassified as pneumonitis ) and its long-term sequelae attributed to a combination of viral persistence, dysregulated immune response (including auto-immunity), endothelial dysfunction and immuno-thrombosis, leading to damage to the lining of small blood vessels and (thence) interference with transfer of oxygen and nutrients to vital organs [ 20 , 21 , 22 , 23 , 24 ]. But most such studies were highly specialized, laboratory-based and written primarily for an audience of fellow laboratory researchers. Despite demonstrating mean differences in a number of metabolic variables, they failed to identify a reliable biomarker that could be used routinely in the clinic to rule a diagnosis of long covid in or out. Whilst the evidence base from laboratory studies grew rapidly, it had little influence on clinical management—partly because most long covid clinics had been set up with impressive speed by front-line clinical teams to address an immediate crisis, with little or no input from immunologists, virologists or metabolic specialists [ 25 ].

Studies of the patient experience revealed wide geographical variation in whether any long covid services were provided and (if they were) which patients were eligible for these and what tests and treatments were available [ 26 ]. An interim UK clinical guideline for long covid had been produced at speed and published in December 2020 [ 27 ], but it was uncertain about diagnostic criteria, investigations, treatments and prognosis. Early policy recommendations for long covid services in England, based on wide consultation across UK, had proposed a tiered service with “tier 1” being supported self-management, “tier 2” generalist assessment and management in primary care, “tier 3” specialist rehabilitation or respiratory follow-up with oversight from a consultant physician and “tier 4” tertiary care for patients with complications or complex needs [ 28 ]. In 2021, ring-fenced funding was allocated to establish 90 multidisciplinary long covid clinics in England [ 29 ]; some clinics were also set up with local funding in Scotland and Wales. These clinics varied widely in eligibility criteria, referral pathways, staffing mix (some had no doctors at all) and investigations and treatments offered. A further policy document on improving long covid services was published in 2022 [ 30 ]; it recommended that specialist long covid clinics should continue, though the long-term funding of these services remains uncertain [ 31 ]. To build the evidence base for delivering long covid services, major programs of publicly funded research were commenced in both UK [ 32 ] and USA [ 33 ].

In short, at the time this study began (late 2021), there appeared to be much scope for a program of quality improvement which would capture fast-emerging research findings, establish evidence-based standards and ensure these were rapidly disseminated and consistently adopted across both specialist long covid services and in primary care.

Quality improvement collaboratives

The quality improvement movement in healthcare was born in the early 1980s when clinicians and policymakers US and UK [ 34 , 35 , 36 , 37 ] began to draw on insights from outside the sector [ 38 , 39 , 40 ]. Adapting a total quality management approach that had previously transformed the Japanese car industry, they sought to improve efficiency, reduce waste, shift to treating the upstream causes of problems (hence preventing disease) and help all services approach the standards of excellence achieved by the best. They developed an approach based on (a) understanding healthcare as a complex system (especially its key interdependencies and workflows), (b) analysing and addressing variation within the system, (c) learning continuously from real-world data and (d) developing leaders who could motivate people and help them change structures and processes [ 41 , 42 , 43 , 44 ].

Quality improvement collaboratives (originally termed “breakthrough collaboratives” [ 45 ]), in which representatives from different healthcare organizations come together to address a common problem, identify best practice, set goals, share data and initiate and evaluate improvement efforts [ 46 ], are one model used to deliver system-wide quality improvement. It is widely assumed that these collaboratives work because—and to the extent that—they identify, interpret and implement high-quality evidence (e.g. from randomized controlled trials).

Research on why quality improvement collaboratives succeed or fail has produced the following list of critical success factors: taking a whole-system approach, selecting a topic and goal that fits with organizations’ priorities, fostering a culture of quality improvement (e.g. that quality is everyone’s job), engagement of everyone (including the multidisciplinary clinical team, managers, patients and families) in the improvement effort, clearly defining people’s roles and contribution, engaging people in preliminary groundwork, providing organizational-level support (e.g. chief executive endorsement, protected staff time, training and support for teams, resources, quality-focused human resource practices, external facilitation if needed), training in specific quality improvement techniques (e.g. plan-do-study-act cycle), attending to the human dimension (including cultivating trust and working to ensure shared vision and buy-in), continuously generating reliable data on both processes (e.g. current practice) and outcomes (clinical, satisfaction) and a “learning system” infrastructure in which knowledge that is generated feeds into individual, team and organizational learning [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ].

The quality improvement collaborative approach has delivered many successes but it has been criticized at a theoretical level for over-simplifying the social science of human motivation and behaviour and for adopting a somewhat mechanical approach to the study of complex systems [ 55 , 56 ]. Adaptations of the original quality improvement methodology (e.g. from Sweden [ 57 , 58 ]) have placed greater emphasis on human values and meaning-making, on the grounds that reducing the complexities of a system-wide quality improvement effort to a set of abstract and generic “success factors” will miss unique aspects of the case such as historical path dependencies, personalities, framing and meaning-making and micropolitics [ 59 ].

Perhaps this explains why, when the abovementioned factors are met, a quality improvement collaborative’s success is more likely but is not guaranteed, as a systematic review demonstrated [ 60 ]. Some well-designed and well-resourced collaboratives addressing clear knowledge gaps produced few or no sustained changes in key outcome measures [ 49 , 53 , 60 , 61 , 62 ]. To identify why this might be, a detailed understanding of a service’s history, current challenges and contextual constraints is needed. This explains our decision, part-way through the study reported here, to collect rich contextual data on participating sites so as to better explain success or failure of our own collaborative.

Warranted and unwarranted variation in clinical practice

A generation ago, Wennberg described most variation in clinical practice as “unwarranted” (which he defined as variation in the utilization of health care services that cannot be explained by variation in patient illness or patient preferences) [ 63 ]. Others coined the term “postcode lottery” to depict how such variation allegedly impacted on health outcomes [ 64 ]. Wennberg and colleagues’ Atlas of Variation , introduced in 1999 [ 65 ], and its UK equivalent, introduced in 2010 [ 66 ], described wide regional differences in the rates of procedures from arthroscopy to hysterectomy, and were used to prompt services to identify and address examples of under-treatment, mis-treatment and over-treatment. Numerous similar initiatives, mostly based on hospital activity statistics, have been introduced around the world [ 66 , 67 , 68 , 69 ]. Sutherland and Levesque’s proposed framework for analysing variation, for example, has three domains: capacity (broadly, whether sufficient resources are allocated at organizational level and whether individuals have the time and headspace to get involved), evidence (the extent to which evidence-based guidelines exist and are followed), and agency (e.g. whether clinicians are engaged with the issue and the effect of patient choice) [ 70 ].

Whilst it is clearly a good idea to identify unwarranted variation in practice, it is also important to acknowledge that variation can be warranted . The very act of measuring and describing variation carries great rhetorical power, since revealing geographical variation in any chosen metric effectively frames this as a problem with a conceptually simple solution (reducing variation) that will appeal to both politicians and the public [ 71 ]. The temptation to expose variation (e.g. via visualizations such as maps) and address it in mechanistic ways should be resisted until we have fully understood the reasons why it exists, which may include perverse incentives, insufficient opportunities to discuss cases with colleagues, weak or absent feedback on practice, unclear decision processes, contested definitions of appropriate care and professional challenges to guidelines [ 72 ].

Research question, aims and objectives

Research question.

What is quality in long covid care and how can it best be achieved?

To identify best practice and reduce unwarranted variation in UK long covid services.

To explain aspects of variation in long covid services that are or may be warranted.

Our original objectives were to:

Establish a quality improvement collaborative for 10 long covid clinics across UK.

Use quality improvement methods in collaboration with patients and clinic staff to prioritize aspects of care to improve. For each priority topic, identify best (evidence-informed) clinical practice, measure performance in each clinic, compare performance with a best practice benchmark and improve performance.

Produce organizational case studies of participating long covid clinics to explain their origins, evolution, leadership, ethos, population served, patient pathways and place in the wider healthcare ecosystem.

Examine these case studies to explain variation in practice, especially in topics where the quality improvement cycle proves difficult to follow or has limited impact.

The LOCOMOTION study

LOCOMOTION (LOng COvid Multidisciplinary consortium Optimising Treatments and services across the NHS) was a 30-month multi-site case study of 10 long covid clinics (8 in England, 1 in Wales and 1 in Scotland), beginning in 2021, which sought to optimise long covid care. Each clinic offered multidisciplinary care to patients referred from primary or secondary care (and, in some cases, self-referred), and held regular multidisciplinary team (MDT) meetings, mostly online via Microsoft Teams, to discuss cases. A study protocol for LOCOMOTION, with details of ethical approvals, management, governance and patient involvement has been published [ 25 ]. The three main work packages addressed quality improvement, technology-supported patient self-management and phenotyping and symptom clustering. This paper reports on the first work package, focusing mainly on qualitative findings.

Setting up the quality improvement collaborative

We broadly followed standard methodology for “breakthrough” quality improvement collaboratives [ 44 , 45 ], with two exceptions. First, because of geographical distance, continuing pandemic precautions and developments in videoconferencing technology, meetings were held online. Second, unlike in the original breakthrough model, patients were included in the collaborative, reflecting the cultural change towards patient partnerships since the model was originally proposed 40 years ago.

Each site appointed a clinical research fellow (doctor, nurse or allied health professional) funded partly by the LOCOMOTION study and partly with clinical sessions; some were existing staff who were backfilled to take on a research role whilst others were new appointments. The quality improvement meetings were held approximately every 8 weeks on Microsoft Teams and lasted about 2 h; there was an agenda and a chair, and meetings were recorded with consent. The clinical research fellow from each clinic attended, sometimes joined by the clinical lead for that site. In the initial meeting, the group proposed and prioritized topics before merging their consensus with the list of priority topics generated separately by patients (there was much overlap but also some differences).

In subsequent meetings, participants attempted to reach consensus on how to define, measure and achieve quality for each priority topic in turn, implement this approach in their own clinic and monitor its impact. Clinical leads prepared illustrative clinical cases and summaries of the research evidence, which they presented using Microsoft Powerpoint; the group then worked towards consensus on the implications for practice through general discussion. Clinical research fellows assisted with literature searches, collected baseline data from their own clinic, prepared and presented anonymized case examples, and contributed to collaborative goal-setting for improvement. Progress on each topic was reviewed at a later meeting after an agreed interval.

An additional element of this work package was semi-structured interviews with 29 patients, recruited from 9 of the 10 participating sites, about their clinic experiences with a view to feeding into service improvement (in the other site, no patient volunteered).

Our patient advisory group initially met separately from the quality improvement collaborative. They designed a short survey of current practice and sent it to each clinic; the results of this informed a prioritization exercise for topics where they considered change was needed. The patient-generated list was tabled at the quality improvement collaborative discussions, but patients were understandably keen to join these discussions directly. After about 9 months, some patient advisory group members joined the regular collaborative meetings. This dynamic was not without its tensions, since sharing performance data requires trust and there were some concerns about confidentiality when real patient cases were discussed with other patients present.

How evidence-informed quality targets were set

At the time the study began, there were no published large-scale randomized controlled trials of any interventions for long covid. We therefore followed a model used successfully in other quality improvement efforts where research evidence was limited or absent or it did not translate unambiguously into models for current services. In such circumstances, the best evidence may be custom and practice in the best-performing units. The quality improvement effort becomes oriented to what one group of researchers called “potentially better practices”—that is, practices that are “developed through analysis of the processes of care, literature review, and site visits” (page 14) [ 73 ]. The idea was that facilitated discussion among clinical teams, drawing on published research where available but also incorporating clinical experience, established practice and systematic analysis of performance data across participating clinics would surface these “potentially better practices”—an approach which, though not formally tested in controlled trials, appears to be associated with improved outcomes [ 46 , 73 ].

Adding an ethnographic component

Following limited progress made on some topics that had been designated high priority, we interviewed all 10 clinical research fellows (either individually or, in two cases, with a senior clinician present) and 18 other clinic staff (five individually plus two groups of 5 and 8), along with additional informal discussions, to explore the challenges of implementing the changes that had been agreed. These interviews were not audiotaped but detailed notes were made and typed up immediately afterwards. It became evident that some aspects of what the collaborative had deemed “evidence-informed” care were contested by front-line clinic staff, perceived as irrelevant to the service they were delivering, or considered impossible to implement. To unpack these issues further, the research protocol was amended to include an ethnographic component.

TG and EL (academic general practitioners) and JLD (a qualitative researcher with a PhD in the patient experience) attended a total of 45 MDT meetings in participating clinics (mostly online or hybrid). Staff were informed in advance that there would be an observer present; nobody objected. We noted brief demographic and clinical details of cases discussed (but no identifying data), dilemmas and uncertainties on which discussions focused, and how different staff members contributed.

TG made 13 in-person visits to participating long covid clinics. Staff were notified in advance; all were happy to be observed. Visits lasted between 5 and 8 h (54 h in total). We observed support staff booking patients in and processing requests and referrals, and shadowed different clinical staff in turn as they saw patients. Patients were informed of our presence and its purpose beforehand and given the opportunity to decline (three of 53 patients approached did). We discussed aspects of each case with the clinician after the patient left. When invited, we took breaks with staff and used these as an opportunity to ask them informally what it was like working in the clinic.

Ethnographic observation, analysis and reporting was geared to generating a rich interpretive account of the clinical, operational and interpersonal features of each clinic—what Van Maanen calls an “impressionist tales” [ 74 ]. Our work was also guided by the principles set out by Golden-Biddle and Locke, namely authenticity (spending time in the field and basing interpretations on these direct observations), plausibility (creating a plausible account through rich persuasive description) and criticality (e.g. reflexively examining our own assumptions) [ 75 ]. Our collection and analysis of qualitative data was informed by our own professional backgrounds (two general practitioners, one physical therapist, two non-clinicians).

In both MDTs and clinics, we took contemporaneous notes by hand and typed these up immediately afterwards.

Data management and analysis

Typed interview notes and field notes from clinics were collated in a set of Word documents, one for each clinic attended. They were analysed thematically [ 76 ] with attention to the literature on quality improvement and variation (see “ Background ”). Interim summaries were prepared on each clinic, setting out the narrative of how it had been established, its ethos and leadership, setting and staffing, population served and key links with other parts of the local healthcare ecosystem.

Minutes and field notes from the quality improvement collaborative meetings were summarized topic by topic, including initial data collected by the researchers-in-residence, improvement actions taken (or attempted) in that clinic, and any follow-up data shared. Progress or lack of it was interpreted in relation to the contextual case summary for that clinic.

Patient cases seen in clinic, and those discussed by MDTs, were summarized as brief case narratives in Word documents. Using the constant comparative method [ 77 ], we produced an initial synthesis of the clinical picture and principles of management based on the first 10 patient cases seen, and refined this as each additional case was added. Demographic and brief clinical and social details were also logged on Excel spreadsheets. When writing up clinical cases, we used the technique of composite case construction (in which we drew on several actual cases to generate a fictitious one, thereby protecting anonymity whilst preserving key empirical findings [ 78 ]); any names reported in this paper are pseudonyms.

Member checking

A summary was prepared for each clinic, including a narrative of the clinic’s own history and a summary of key quality issues raised across the ten clinics. These summaries included examples from real cases in our dataset. These were shared with the clinical research fellow and a senior clinician from the clinic, and amended in response to feedback. We also shared these summaries with representatives from the patient advisory group.

Overview of dataset

This study generated three complementary datasets. First, the video recordings, minutes, and field notes of 12 quality improvement collaborative meetings, along with the evidence summaries prepared for these meetings and clinic summaries (e.g. descriptions of current practice, audits) submitted by the clinical research fellows. This dataset illustrated wide variation in practice, and (in many topics) gaps or ambiguities in the evidence base.

Second, interviews with staff ( n  = 30) and patients ( n  = 29) from the clinics, along with ethnographic field notes (approximately 100 pages) from 13 in-person clinic visits (54 h), including notes on 50 patient consultations (40 face-to-face, 6 telephone, 4 video). This dataset illustrated the heterogeneity among the ten participating clinics.

Third, field notes (approximately 100 pages), including discussions on 244 clinical cases from the 45 MDT meetings (49 h) that we observed. This dataset revealed further similarities and contrasts among clinics in how patients were managed. In particular, it illustrated how, for the complex patients whose cases were presented at these meetings, teams made sense of, and planned for, each case through multidisciplinary dialogue. This dialogue typically began with one staff member presenting a detailed clinical history along with a narrative of how it had affected the patient’s life and what was at stake for them (e.g. job loss), after which professionals from various backgrounds (nursing, physical therapy, occupational therapy, psychology, dietetics, and different medical specialties) joined in a discussion about what to do.

The ten participating sites are summarized in Table  1 .

In the next two sections, we explore two issues—difficulty defining best practice and the heterogeneous nature of the clinics—that were key to explaining why quality, when pursued in a 10-site collaborative, proved elusive. We then briefly summarize patients’ accounts of their experience in the clinics and give three illustrative examples of the elusiveness of quality improvement using selected topics that were prioritized in our collaborative: outcome measures, investigation of palpitations and management of fatigue. In the final section of the results, we describe how MDT deliberations proved crucial for local quality improvement. Further detail on clinical priority topics will be presented in a separate paper.

“Best practice” in long covid: uncertainty and conflict

The study period (September 2021 to December 2023) corresponded with an exponential increase in published research on long covid. Despite this, the quality improvement collaborative found few unambiguous recommendations for practice. This gap between what the research literature offered and what clinical practice needed was partly ontological (relating what long covid is ). One major bone of contention between patients and clinicians (also evident in discussions with our patient advisory group), for example, was how far (and in whom) clinicians should look for and attempt to treat the various metabolic abnormalities that had been documented in laboratory research studies. The literature on this topic was extensive but conflicting [ 20 , 21 , 22 , 23 , 24 , 79 , 80 , 81 , 82 ]; it was heavy on biological detail but light on clinical application.

Patients were often aware of particular studies that appeared to offer plausible molecular or cellular explanations for symptom clusters along with a drug (often repurposed and off-label) whose mechanism of action appeared to be a good fit with the metabolic chain of causation. In one clinic, for example, we were shown an email exchange between a patient (not medically qualified) and a consultant, in which the patient asked them to reconsider their decision not to prescribe low-dose naltrexone, an opioid receptor antagonist with anti-inflammatory properties. The request included a copy of a peer-reviewed academic paper describing a small, uncontrolled pre-post study (i.e. a weak study design) in which this drug appeared to improve symptoms and functional performance in patients with long covid, as well as a mechanistic argument explaining why the patient felt this drug was a plausible choice in their own case.

This patient’s clinician, in common with most clinicians delivering front-line long covid services, considered that the evidence for such mechanism-based therapies was weak. Clinicians generally felt that this evidence, whilst promising, did not yet support routine measurement of clotting factors, antibodies, immune cells or other biomarkers or the prescription of mechanism-based therapies such as antivirals, anti-inflammatories or anticoagulants. Low-dose naltroxone, for example, is currently being tested in at least one randomized controlled trial (see National Clinical Trials Registry NCT05430152), which had not reported at the time of our observations.

Another challenge to defining best practice was the oft-repeated phrase that long covid is a “diagnosis by exclusion”, but the high prevalence of comorbidities meant that the “pure” long covid patient untainted by other potential explanations for their symptoms was a textbook ideal. In one MDT, for example, we observed a discussion about a patient who had had both swab-positive covid-19 and erythema migrans (a sign of Lyme disease) in the weeks before developing fatigue, yet local diagnostic criteria for each condition required the other to be excluded.

The logic of management in most participating clinics was pragmatic: prompt multidisciplinary assessment and treatment with an emphasis on obtaining a detailed clinical history (including premorbid health status), excluding serious complications (“red flags”), managing specific symptom clusters (for example, physical therapy for breathing pattern disorder), treating comorbidities (for example, anaemia, diabetes or menopause) and supporting whole-person rehabilitation [ 7 , 83 ]. The evidentiary questions raised in MDT discussions (which did not include patients) addressed the practicalities of the rehabilitation model (for example, whether cognitive therapy for neurocognitive complications is as effective when delivered online as it is when delivered in-person) rather than the molecular or cellular mechanisms of disease. For example, the question of whether patients with neurocognitive impairment should be tested for micro-clots or treated with anticoagulants never came up in the MDTs we observed, though we did visit a tertiary referral clinic (the tier 4 clinic in site H), whose lead clinician had a research interest in inflammatory coagulopathies and offered such tests to selected patients.

Because long covid typically produces dozens of symptoms that tend to be uniquely patterned in each patient, the uncertainties on which MDT discussions turned were rarely about general evidence of the kind that might be found in a guideline (e.g. how should fatigue be managed?). Rather they concerned particular case-based clinical decisions (e.g. how should this patient’s fatigue be managed, given the specifics of this case?). An example from our field notes illustrates this:

Physical therapist presents the case of a 39-year-old woman who works as a cleaner on an overnight ferry. Has had long covid for 2 years. Main symptoms are shortness of breath and possible anxiety attacks, especially when at work. She has had a course of physical therapy to teach diaphragmatic breathing but has found that focusing on her breathing makes her more anxious. Patient has to do a lot of bending in her job (e.g. cleaning toilets and under seats), which makes her dizzy, but Active Stand Test was normal. She also has very mild tricuspid incompetence [someone reads out a cardiology report—not hemodynamically significant].

Rehabilitation guidelines (e.g. WHO) recommend phased return to work (e.g. with reduced hours) and frequent breaks. “Tricky!” says someone. The job is intense and busy, and the patient can’t afford not to work. Discussion on whether all her symptoms can be attributed to tension and anxiety. Physical therapist who runs the breathing group says, “No, it’s long covid”, and describes severe initial covid-19 episode and results of serial chest X-rays which showed gradual clearing of ground glass shadows. Team discussion centers on how to negotiate reduced working hours in this particular job, given the overnight ferry shifts. --MDT discussion, Site D

This example raises important considerations about the nature of clinical knowledge in long covid. We return to it in the final section of the “ Results ” and in the “ Discussion ”.

Long covid clinics: a heterogeneous context for quality improvement

Most participating clinics had been established in mid-2020 to follow up patients who had been hospitalized (and perhaps ventilated) for severe acute covid-19. As mass vaccination reduced the severity of acute covid-19 for most people, the patient population in all clinics progressively shifted to include fewer “post-ICU [intensive care unit]” patients (in whom respiratory symptoms almost always dominated), and more people referred by their general practitioners or other secondary care specialties who had not been hospitalized for their acute covid-19 infection, and in whom fatigue, brain fog and palpitations were often the most troubling symptoms. Despite these similarities, the ten clinics had very different histories, geographical and material settings, staffing structures, patient pathways and case mix, as Table  1 illustrates. Below, we give more detail on three example sites.

Site C was established as a generalist “assessment-only” service by a general practitioner with an interest in infectious diseases. It is led jointly by that general practitioner and an occupational therapist, assisted by a wide range of other professionals including speech and language therapy, dietetics, clinical psychology and community-based physical therapy and occupational therapy. It has close links with a chronic fatigue service and a pain clinic that have been running in the locality for over 20 years. The clinic, which is entirely virtual (staff consult either from home or from a small side office in the community trust building), is physically located in a low-rise building on the industrial outskirts of a large town, sharing office space with various community-based health and social care services. Following a 1-h telephone consultation by one of the clinical leads, each patient is discussed at the MDT and then either discharged back to their general practitioner with a detailed management plan or referred on to one of the specialist services. This arrangement evolved to address a particular problem in this locality—that many patients with long covid were being referred by their general practitioner to multiple specialties (e.g. respiratory, neurology, fatigue), leading to a fragmented patient experience, unnecessary specialist assessments and wasteful duplication. The generalist assessment by telephone is oriented to documenting what is often a complex illness narrative (including pre-existing physical and mental comorbidities) and working with the patient to prioritize which symptoms or problems to pursue in which order.

Site E, in a well-regarded inner-city teaching hospital, had been set up in 2020 by a respiratory physician. Its initial ethos and rationale had been “respiratory follow-up”, with strong emphasis on monitoring lung damage via repeated imaging and lung function tests and in ensuring that patients received specialist physical therapy to “re-learn” efficient breathing techniques. Over time, this site has tried to accommodate a more multi-system assessment, with the introduction of a consultant-led infectious disease clinic for patients without a dominant respiratory component, reflecting the shift towards a more fatigue-predominant case mix. At the time of our fieldwork, each patient was seen in turn by a physician, psychologist, occupational therapist and respiratory physical therapist (half an hour each) before all four staff reconvened in a face-to-face MDT meeting to form a plan for each patient. But whilst a wide range of patients with diverse symptoms were discussed at these meetings, there remained a strong focus on respiratory pathology (e.g. tracking improvements in lung function and ensuring that coexisting asthma was optimally controlled).

Site F, one of the first long covid clinics in UK, was set up by a rehabilitation consultant who had been drafted to work on the ICU during the first wave of covid-19 in early 2020. He had a longstanding research interest in whole-patient rehabilitation, especially the assessment and management of chronic fatigue and pain. From the outset, clinic F was more oriented to rehabilitation, including vocational rehabilitation to help patients return to work. There was less emphasis on monitoring lung function or pursuing respiratory comorbidities. At the time of our fieldwork, clinic F offered both a community-based service (“tier 2”) led by an occupational therapist, supported by a respiratory physical therapist and psychologist, and a hospital-based service (“tier 3”) led by the rehabilitation consultant, supported by a wider MDT. Staff in both tiers emphasized that each patient needs a full physical and mental assessment and help to set and work towards achievable goals, whilst staying within safe limits so as to avoid post-exertional symptom exacerbation. Because of the research interest of the lead physician, clinic F adapted well to the growing numbers of patients with fatigue and quickly set up research studies on this cohort [ 84 ].

Details of the other seven sites are shown in Table  1 . Broadly speaking, sites B, E, G and H aligned with the “respiratory follow-up” model and sites F and I aligned with the “rehabilitation” model. Sites A and J had a high-volume, multi-tiered service whose community tier aligned with the “holistic GP assessment” model (site C above) and which also offered a hospital-based, rehabilitation-focused tier. The small service in Scotland (site D) had evolved from an initial respiratory focus to become part of the infectious diseases (ME/CFS) service; Lyme disease (another infectious disease whose sequelae include chronic fatigue) was also prevalent in this region.

The patient experience

Whilst the 10 participating clinics were very diverse in staffing, ethos and patient flows, the 29 patient interviews described remarkably consistent clinic experiences. Almost all identified the biggest problem to be the extended wait of several months before they were seen and the limited awareness (when initially referred) of what long covid clinics could provide. Some talked of how they cried with relief when they finally received an appointment. When the quality improvement collaborative was initially established, waiting times and bottlenecks were patients’ the top priority for quality improvement, and this ranking was shared by clinic staff, who were very aware of how much delays and uncertainties in assessment and treatment compounded patients’ suffering. This issue resolved to a large extent over the study period in all clinics as the referral backlog cleared and the incidence of new cases of long covid fell [ 85 ]; it will be covered in more detail in a separate publication.

Most patients in our sample were satisfied with the care they received when they were finally seen in clinic, especially how they finally felt “heard” after a clinician took a full history. They were relieved to receive affirmation of their experience, a diagnosis of what was wrong and reassurance that they were believed. They were grateful for the input of different members of the multidisciplinary teams and commented on the attentiveness, compassion and skill of allied professionals in particular (“she was wonderful, she got me breathing again”—patient BIR145 talking about a physical therapist). One or two patient participants expressed confusion about who exactly they had seen and what advice they had been given, and some did not realize that a telephone assessment had been an actual clinical consultation. A minority expressed disappointment that an expected investigation had not been ordered (one commented that they had not had any blood tests at all). Several had assumed that the help and advice from the long covid clinic would continue to be offered until they were better and were disappointed that they had been discharged after completing the various courses on offer (since their clinic had been set up as an “assessment only” service).

In the next sections, we give examples of topics raised in the quality improvement collaborative and how they were addressed.

Example quality topic 1: Outcome measures

The first topic considered by the quality improvement collaborative was how (that is, using which measures and metrics) to assess and monitor patients with long covid. In the absence of a validated biomarker, various symptom scores and quality of life scales—both generic and disease-specific—were mooted. Site F had already developed and validated a patient-reported outcome measure (PROM), the C19-YRS (Covid-19 Yorkshire Rehabilitation Scale) and used it for both research and clinical purposes [ 86 ]. It was quickly agreed that, for the purposes of generating comparative research findings across the ten clinics, the C19-YRS should be used at all sites and completed by patients three-monthly. A commercial partner produced an electronic version of this instrument and an app for patient smartphones. The quality improvement collaborative also agreed that patients should be asked to complete the EUROQOL EQ5D, a widely used generic health-related quality of life scale [ 87 ], in order to facilitate comparisons between long covid and other chronic conditions.

In retrospect, the discussions which led to the unopposed adoption of these two measures as a “quality” initiative in clinical care were somewhat aspirational. A review of progress at a subsequent quality improvement meeting revealed considerable variation among clinics, with a wide variety of measures used in different clinics to different degrees. Reasons for this variation were multiple. First, although our patient advisory group were keen that we should gather as much data as possible on the patient experience of this new condition, many clinic patients found the long questionnaires exhausting to complete due to cognitive impairment and fatigue. In addition, whilst patients were keen to answer questions on symptoms that troubled them, many had limited patience to fill out repeated surveys on symptoms that did not trouble them (“it almost felt as if I’ve not got long covid because I didn’t feel like I fit the criteria as they were laying it out”—patient SAL001). Staff assisted patients in completing the measures when needed, but this was time-consuming (up to 45 min per instrument) and burdensome for both staff and patients. In clinics where a high proportion of patients required assistance, staff time was the rate-limiting factor for how many instruments got completed. For some patients, one short instrument was the most that could be asked of them, and the clinician made a judgement on which one would be in their best interests on the day.

The second reason for variation was that the clinical diagnosis and management of particular features, complications and comorbidities of long covid required more nuance than was provided by these relatively generic instruments, and the level of detail sought varied with the specialist interest of the clinic (and the clinician). The modified C19-YRS [ 88 ], for example, contained 19 items, of which one asked about sleep quality. But if a patient had sleep difficulties, many clinicians felt that these needed to be documented in more detail—for example using the 8-item Epworth Sleepiness Scale, originally developed for conditions such as narcolepsy and obstructive sleep apnea [ 89 ]. The “Epworth score” was essential currency for referrals to some but not all specialist sleep services. Similarly, the C19-YRS had three items relating to anxiety, depression and post-traumatic stress disorder, but in clinics where there was a strong focus on mental health (e.g. when there was a resident psychologist), patients were usually invited to complete more specific tools (e.g. the Patient Health Questionnaire 9 [ 90 ], a 9-item questionnaire originally designed to assess severity of depression).

The third reason for variation was custom and practice. Ethnographic visits revealed that paper copies of certain instruments were routinely stacked on clinicians’ desks in outpatient departments and also (in some cases) handed out by administrative staff in waiting areas so that patients could complete them before seeing the clinician. These familiar clinic artefacts tended to be short (one-page) instruments that had a long tradition of use in clinical practice. They were not always fit for purpose. For example, the Nijmegen questionnaire was developed in the 1980s to assess hyperventilation; it was validated against a longer, “gold standard” instrument for that condition [ 91 ]. It subsequently became popular in respiratory clinics to diagnose or exclude breathing pattern disorder (a condition in which the normal physiological pattern of breathing becomes replaced with less efficient, shallower breathing [ 92 ]), so much so that the researchers who developed the instrument published a paper to warn fellow researchers that it had not been validated for this purpose [ 93 ]. Whilst a validated 17-item instrument for breathing pattern disorder (the Self-Evaluation of Breathing Questionnaire [ 94 ]) does exist, it is not in widespread clinical use. Most clinics in LOCOMOTION used Nijmegen either on all patients (e.g. as part of a comprehensive initial assessment, especially if the service had begun as a respiratory follow-up clinic) or when breathing pattern disorder was suspected.

In sum, the use of outcome measures in long covid clinics was a compromise between standardization and contingency. On the one hand, all clinics accepted the need to use “validated” instruments consistently. On the other hand, there were sometimes good reasons why they deviated from agreed practice, including mismatch between the clinic’s priorities as a research site, its priorities as a clinical service, and the particular clinical needs of a patient; the clinic’s—and the clinician’s—specialist focus; and long-held traditions of using particular instruments with which staff and patients were familiar.

Example quality topic 2: Postural orthostatic tachycardia syndrome (POTS)

Palpitations (common in long covid) and postural orthostatic tachycardia syndrome (POTS, a disproportionate acceleration in heart rate on standing, the assumed cause of palpitations in many long covid patients) was the top priority for quality improvement identified by our patient advisory group. Reflecting discussions and evidence (of various kinds) shared in online patient communities, the group were confident that POTS is common in long covid patients and that many cases remain undetected (perhaps misdiagnosed as anxiety). Their request that all long covid patients should be “screened” for POTS prompted a search for, and synthesis of, evidence (which we published in the BMJ [ 95 ]). In sum, that evidence was sparse and contested, but, combined with standard practice in specialist clinics, broadly supported the judicious use of the NASA Lean Test [ 96 ]. This test involves repeated measurements of pulse and blood pressure with the patient first lying and then standing (with shoulders resting against a wall).

The patient advisory group’s request that the NASA Lean Test should be conducted on all patients met with mixed responses from the clinics. In site F, the lead physician had an interest in autonomic dysfunction in chronic fatigue and was keen; he had already published a paper on how to adapt the NASA Lean Test for self-assessment at home [ 97 ]. Several other sites were initially opposed. Staff at site E, for example, offered various arguments:

The test is time-consuming, labor-intensive, and takes up space in the clinic which has an opportunity cost in terms of other potential uses;

The test is unvalidated and potentially misleading (there is a high incidence of both false negative and false positive results);

There is no proven treatment for POTS, so there is no point in testing for it;

It is a specialist test for a specialist condition, so it should be done in a specialist clinic where its benefits and limitations are better understood;

Objective testing does not change clinical management since what we treat is the patient’s symptoms (e.g. by a pragmatic trial of lifestyle measures and medication);

People with symptoms suggestive of dysautonomia have already been “triaged out” of this clinic (that is, identified in the initial telephone consultation and referred directly to neurology or cardiology);

POTS is a manifestation of the systemic nature of long covid; it does not need specific treatment but will improve spontaneously as the patient goes through standard interventions such as active pacing, respiratory physical therapy and sleep hygiene;

Testing everyone, even when asymptomatic, runs counter to the ethos of rehabilitation, which is to “de-medicalize” patients so as to better orient them to their recovery journey.

When clinics were invited to implement the NASA Lean Test on a consecutive sample of patients to resolve a dispute about the incidence of POTS (from “we’ve only seen a handful of people with it since the clinic began” to “POTS is common and often missed”), all but one site agreed to participate. The tertiary POTS centre linked to site H was already running the NASA Lean Test as standard on all patients. Site C, which operated entirely virtually, passed the work to the referring general practitioner by making this test a precondition for seeing the patient; site D, which was largely virtual, sent instructions for patients to self-administer the test at home.

The NASA Lean Test study has been published separately [ 98 ]. In sum, of 277 consecutive patients tested across the eight clinics, 20 (7%) had a positive NASA Lean Test for POTS and a further 28 (10%) a borderline result. Six of 20 patients who met the criteria for POTS on testing had no prior history of orthostatic intolerance. The question of whether this test should be used to “screen” all patients was not answered definitively. But the experience of participating in the study persuaded some sceptics that postural changes in heart rate could be severe in some long covid patients, did not appear to be fully explained by their previously held theories (e.g. “functional”, anxiety, deconditioning), and had likely been missed in some patients. The outcome of this particular quality improvement cycle was thus not a wholescale change in practice (for which the evidence base was weak) but a more subtle increase in clinical awareness, a greater willingness to consider testing for POTS and a greater commitment to contribute to research into this contested condition.

More generally, the POTS audit prompted some clinicians to recognize the value of quality improvement in novel clinical areas. One physician who had initially commented that POTS was not seen in their clinic, for example, reflected:

“ Our clinic population is changing. […] Overall there’s far fewer post-ICU patients with ECMO [extra-corporeal membrane oxygenation] issues and far more long covid from the community, and this is the bit our clinic isn’t doing so well on. We’re doing great on breathing pattern disorder; neuro[logists] are helping us with the brain fogs; our fatigue and occupational advice is ok but some of the dysautonomia symptoms that are more prevalent in the people who were not hospitalized – that’s where we need to improve .” -Respiratory physician, site G (from field visit 6.6.23)

Example quality topic 3: Management of fatigue

Fatigue was the commonest symptom overall and a high priority among both patients and clinicians for quality improvement. It often coexisted with the cluster of neurocognitive symptoms known as brain fog, with both conditions relapsing and remitting in step. Clinicians were keen to systematize fatigue management using a familiar clinical framework oriented around documenting a full clinical history, identifying associated symptoms, excluding or exploring comorbidities and alternative explanations (e.g. poor sleep patterns, depression, menopause, deconditioning), assessing how fatigue affects physical and mental function, implementing a program of physical and cognitive therapy that was sensitive to the patient’s condition and confidence level, and monitoring progress using validated patient-reported outcome measures and symptom diaries.

The underpinning logic of this approach, which broadly reflected World Health Organization guidance [ 99 ], was that fatigue and linked cognitive impairment could be a manifestation of many—perhaps interacting—conditions but that a whole-patient (body and mind) rehabilitation program was the cornerstone of management in most cases. Discussion in the quality improvement collaborative focused on issues such as whether fatigue was so severe that it produced safety concerns (e.g. in a person’s job or with childcare), the pros and cons of particular online courses such as yoga, relaxation and mindfulness (many were viewed positively, though the evidence base was considered weak), and the extent to which respiratory physical therapy had a crossover impact on fatigue (systematic reviews suggested that it may do, but these reviews also cautioned that primary studies were sparse, methodologically flawed, and heterogeneous [ 100 , 101 ]). They also debated the strengths and limitations of different fatigue-specific outcome measures, each of which had been developed and validated in a different condition, with varying emphasis on cognitive fatigue, physical fatigue, effect on daily life, and motivation. These instruments included the Modified Fatigue Impact Scale; Fatigue Severity Scale [ 102 ]; Fatigue Assessment Scale; Functional Assessment Chronic Illness Therapy—Fatigue (FACIT-F) [ 103 ]; Work and Social Adjustment Scale [ 104 ]; Chalder Fatigue Scale [ 105 ]; Visual Analogue Scale—Fatigue [ 106 ]; and the EQ5D [ 87 ]. In one clinic (site F), three of these scales were used in combination for reasons discussed below.

Some clinicians advocated melatonin or nutritional supplements (such as vitamin D or folic acid) for fatigue on the grounds that many patients found them helpful and formal placebo-controlled trials were unlikely ever to be conducted. But neurostimulants used in other fatigue-predominant conditions (e.g. brain injury, stroke), which also lacked clinical trial evidence in long covid, were viewed as inappropriate in most patients because of lack of evidence of clear benefit and hypothetical risk of harm (e.g. adverse drug reactions, polypharmacy).

Whilst the patient advisory group were broadly supportive of a whole-patient rehabilitative approach to fatigue, their primary concern was fatiguability , especially post-exertional symptom exacerbation (PESE, also known as “crashes”). In these, the patient becomes profoundly fatigued some hours or days after physical or mental exertion, and this state can last for days or even weeks [ 107 ]. Patients viewed PESE as a “red flag” symptom which they felt clinicians often missed and sometimes caused. They wanted the quality improvement effort to focus on ensuring that all clinicians were aware of the risks of PESE and acted accordingly. A discussion among patients and clinicians at a quality improvement collaborative meeting raised a new research hypothesis—that reducing the number of repeated episodes of PESE may improve the natural history of long covid.

These tensions around fatigue management played out differently in different clinics. In site C (the GP-led virtual clinic run from a community hub), fatigue was viewed as one manifestation of a whole-patient condition. The lead general practitioner used the metaphor of untangling a skein of wool: “you have to find the end and then gently pull it”. The underlying problem in a fatigued patient, for example, might be an undiagnosed physical condition such as anaemia, disturbed sleep, or inadequate pacing. These required (respectively) the chronic fatigue service (comprising an occupational therapist and specialist psychologist and oriented mainly to teaching the techniques of goal-setting and pacing), a “tiredness” work-up (e.g. to exclude anaemia or menopause), investigation of poor sleep (which, not uncommonly, was due to obstructive sleep apnea), and exploration of mental health issues.

In site G (a hospital clinic which had evolved from a respiratory service), patients with fatigue went through a fatigue management program led by the occupational therapist with emphasis on pacing, energy conservation, avoidance of PESE and sleep hygiene. Those without ongoing respiratory symptoms were often discharged back to their general practitioner once they had completed this; there was no consultant follow-up of unresolved fatigue.

In site F (a rehabilitation clinic which had a longstanding interest in chronic fatigue even before the pandemic), active interdisciplinary management of fatigue was commenced at or near the patient’s first visit, on the grounds that the earlier this began, the more successful it would be. In this clinic, patients were offered a more intensive package: a similar occupational therapy-led fatigue course as those in site G, plus input from a dietician to advise on regular balanced meals and caffeine avoidance and a group-based facilitated peer support program which centred on fatigue management. The dietician spoke enthusiastically about how improving diet in longstanding long covid patients often improved fatigue (e.g. because they had often lost muscle mass and tended to snack on convenience food rather than make meals from scratch), though she agreed there was no evidence base from trials to support this approach.

Pursuing local quality improvement through MDTs

Whilst some long covid patients had “textbook” symptoms and clinical findings, many cases were unique and some were fiendishly complex. One clinician commented that, somewhat paradoxically, “easy cases” were often the post-ICU follow-ups who had resolving chest complications; they tended to do well with a course of respiratory physical therapy and a return-to-work program. Such cases were rarely brought to MDT meetings. “Difficult cases” were patients who had not been hospitalized for their acute illness but presented with a months- or years-long history of multiple symptoms with fatigue typically predominant. Each one was different, as the following example (some details of which have been fictionalized to protect anonymity) illustrates.

The MDT is discussing Mrs Fermah, a 65-year-old homemaker who had covid-19 a year ago. She has had multiple symptoms since, including fluctuating fatigue, brain fog, breathlessness, retrosternal chest pain of burning character, dry cough, croaky voice, intermittent rashes (sometimes on eating), lips going blue, ankle swelling, orthopnoea, dizziness with the room spinning which can be triggered by stress, low back pain, aches and pains in the arms and legs and pins and needles in the fingertips, loss of taste and smell, palpitations and dizziness (unclear if postural, but clear association with nausea), headaches on waking, and dry mouth. She is somewhat overweight (body mass index 29) and admits to low mood. Functionally, she is mostly confined to the house and can no longer manage the stairs so has begun to sleep downstairs. She has stumbled once or twice but not fallen. Her social life has ceased and she rarely has the energy to see her grandchildren. Her 70-year-old husband is retired and generally supportive, though he spends most evenings at his club. Comorbidities include glaucoma which is well controlled and overseen by an ophthalmologist, mild club foot (congenital) and stage 1 breast cancer 20 years ago. Various tests, including a chest X-ray, resting and exercise oximetry and a blood panel, were normal except for borderline vitamin D level. Her breathing questionnaire score suggests she does not have breathing pattern disorder. ECG showed first-degree atrioventricular block and left axis deviation. No clinician has witnessed the blue lips. Her current treatment is online group respiratory physical therapy; a home visit is being arranged to assess her climbing stairs. She has declined a psychologist assessment. The consultant asks the nurse who assessed her: “Did you get a feel if this is a POTS-type dizziness or an ENT-type?” She sighs. “Honestly it was hard to tell, bless her.”—Site A MDT

This patient’s debilitating symptoms and functional impairments could all be due to long covid, yet “evidence-based” guidance for how to manage her complex suffering does not exist and likely never will exist. The question of which (if any) additional blood or imaging tests to do, in what order of priority, and what interventions to offer the patient will not be definitively answered by consulting clinical trials involving hundreds of patients, since (even if these existed) the decision involves weighing this patient’s history and the multiple factors and uncertainties that are relevant in her case. The knowledge that will help the MDT provide quality care to Mrs Fermah is case-based knowledge—accumulated clinical experience and wisdom from managing and deliberating on multiple similar cases. We consider case-based knowledge further in the “ Discussion ”.

Summary of key findings

This study has shown that a quality improvement collaborative of UK long covid clinics made some progress towards standardizing assessment and management in some topics, but some variation remained. This could be explained in part by the fact that different clinics had different histories and path dependencies, occupied a different place in the local healthcare ecosystem, served different populations, were differently staffed, and had different clinical interests. Our patient advisory group and clinicians in the quality improvement collaborative broadly prioritized the same topics for improvement but interpreted them somewhat differently. “Quality” long covid care had multiple dimensions, relating to (among other things) service set-up and accessibility, clinical provision appropriate to the patient’s need (including options for referral to other services locally), the human qualities of clinical and support staff, how knowledge was distributed across (and accessible within) the system, and the accumulated collective wisdom of local MDTs in dealing with complex cases (including multiple kinds of specialist expertise as well as relational knowledge of what was at stake for the patient). Whilst both staff and patients were keen to contribute to the quality improvement effort, the burden of measurement was evident: multiple outcome measures, used repeatedly, were resource-intensive for staff and exhausting for patients.

Strengths and limitations of this study

To our knowledge, we are the first to report both a quality improvement collaborative and an in-depth qualitative study of clinical work in long covid. Key strengths of this work include the diverse sampling frame (with sites from three UK jurisdictions and serving widely differing geographies and demographics); the use of documents, interviews and reflexive interpretive ethnography to produce meaningful accounts of how clinics emerged and how they were currently organized; the use of philosophical concepts to analyse data on how MDTs produced quality care on a patient-by-patient basis; and the close involvement of patient co-researchers and coauthors during the research and writing up.

Limitations of the study include its exclusive UK focus (the external validity of findings to other healthcare systems is unknown); the self-selecting nature of participants in a quality improvement collaborative (our patient advisory group suggested that the MDTs observed in this study may have represented the higher end of a quality spectrum, hence would be more likely than other MDTs to adhere to guidelines); and the particular perspective brought by the researchers (two GPs, a physical therapist and one non-clinical person) in ethnographic observations. Hospital specialists or organizational scholars, for example, may have noticed different things or framed what they observed differently.

Explaining variation in long covid care

Sutherland and Levesque’s framework mentioned in the “ Background ” section does not explain much of the variation found in our study [ 70 ]. In terms of capacity, at the time of this study most participating clinics benefited from ring-fenced resources. In terms of evidence, guidelines existed and were not greatly contested, but as illustrated by the case of Mrs Fermah above, many patients were exceptions to the guideline because of complex symptomatology and relevant comorbidities. In terms of agency, clinicians in most clinics were passionately engaged with long covid (they were pioneers who had set up their local clinic and successfully bid for national ring-fenced resources) and were generally keen to support patient choice (though not if the patient requested tests which were unavailable or deemed not indicated).

Astma et al.’s list of factors that may explain variation in practice (see “ Background ”) includes several that may be relevant to long covid, especially that the definition of appropriate care in this condition remains somewhat contested. But lack of opportunity to discuss cases was not a problem in the clinics in our sample. On the contrary, MDT meetings in each locality gave clinicians multiple opportunities to discuss cases with colleagues and reflect collectively on whether and how to apply particular guidelines.

The key problem was not that clinicians disputed the guidelines for managing long covid or were unaware of them; it was that the guidelines were not self-interpreting . Rather, MDTs had to deliberate on the balance of benefits and harms in different aspects of individual cases. In patients whose symptoms suggested a possible diagnosis of POTS (or who suspected themselves of having POTS), for example, these deliberations were sometimes lengthy and nuanced. Should a test result that is not technically in the abnormal range but close to it be treated as diagnostic, given that symptoms point to this diagnosis? If not, should the patient be told that the test excludes POTS or that it is equivocal? If a cardiology opinion has stated firmly that the patient does not have POTS but the cardiologist is not known for their interest in this condition, should a second specialist opinion be sought? If the gold standard “tilt test” [ 108 ] for POTS (usually available only in tertiary centres) is not available locally, does this patient merit a costly out-of-locality referral? Should the patient’s request for a trial of off-label medication, reflecting discussions in an online support group, be honoured? These are the kinds of questions on which MDTs deliberated at length.

The fact that many cases required extensive deliberation does not necessarily justify variation in practice among clinics. But taking into account the clinics’ very different histories, set-up, and local referral pathways, the variation begins to make sense. A patient who is being assessed in a clinic that functions as a specialist chronic fatigue centre and attracts referrals which reflect this interest (e.g. site F in our sample) will receive different management advice from one that functions as a telephone-only generalist assessment centre and refers on to other specialties (site C in our sample). The wide variation in case mix, coupled with the fact that a different proportion of these cases were highly complex in each clinic (and in different ways), suggests that variation in practice may reflect appropriate rather than inappropriate care.

Our patient advisory group affirmed that many of the findings reported here resonated with their own experience, but they raised several concerns. These included questions about patient groups who may have been missed in our sample because they were rarely discussed in MDTs. The decision to take a case to MDT discussion is taken largely by a clinician, and there was evidence from online support groups that some patients’ requests for their case to be taken to an MDT had been declined (though not, to our knowledge, in the clinics participating in the LOCOMOTION study).

We began this study by asking “what is quality in long covid care?”. We initially assumed that this question referred to a generalizable evidence base, which we felt we could identify, and we believed that we could then determine whether long covid clinics were following the evidence base through conventional audits of structure, process, and outcome. In retrospect, these assumptions were somewhat naïve. On the basis of our findings, we suggest that a better (and more individualized) research question might be “to what extent does each patient with long covid receive evidence-based care appropriate to their needs?”. This question would require individual case review on a sample of cases, tracking each patient longitudinally including cross-referrals, and also interviewing the patient.

Nomothetic versus idiographic knowledge

In a series of lectures first delivered in the 1950s and recently republished [ 109 ], psychiatrist Dr Maurice O’Connor Drury drew on the later philosophy of his friend and mentor Ludwig Wittgenstein to challenge what he felt was a concerning trend: that the nomothetic (generalizable, abstract) knowledge from randomized controlled trials (RCTs) was coming to over-ride the idiographic (personal, situated) knowledge about particular patients. Based on Wittgenstein’s writings on the importance of the particular, Drury predicted—presciently—that if implemented uncritically, RCTs would result in worse, not better, care for patients, since it would go hand-in-hand with a downgrading of experience, intuition, subjective judgement, personal reflection, and collective deliberation.

Much conventional quality improvement methodology is built on an assumption that nomothetic knowledge (for example, findings from RCTs and systematic reviews) is a higher form of knowing than idiographic knowledge. But idiographic, case-based reasoning—despite its position at the very bottom of evidence-based medicine’s hierarchy of evidence [ 110 ]—is a legitimate and important element of medical practice. Bioethicist Kathryn Montgomery, drawing on Aristotle’s notion of praxis , considers clinical practice to be an example of case-based reasoning [ 111 ]. Medicine is governed not by hard and fast laws but by competing maxims or rules of thumb ; the essence of judgement is deciding which (if any) rule should be applied in a particular circumstance. Clinical judgement incorporates science (especially the results of well-conducted research) and makes use of available tools and technologies (including guidelines and decision-support algorithms that incorporate research findings). But rather than being determined solely by these elements, clinical judgement is guided both by the scientific evidence and by the practical and ethical question “what is it best to do, for this individual, given these circumstances?”.

In this study, we observed clinical management of, and MDT deliberations on, hundreds of clinical cases. In the more straightforward ones (for example, recovering pneumonitis), guideline-driven care was not difficult to implement and such cases were rarely brought to the MDT. But cases like Mrs Fermah (see last section of “ Results ”) required much discussion on which aspects of which guideline were in the patient’s best interests to bring into play at any particular stage in their illness journey.

Conclusions

One systematic review on quality improvement collaboratives concluded that “ [those] reporting success generally addressed relatively straightforward aspects of care, had a strong evidence base and noted a clear evidence-practice gap in an accepted clinical pathway or guideline” (page 226) [ 60 ]. The findings from this study suggest that to the extent that such collaboratives address clinical cases that are not straightforward, conventional quality improvement methods may be less useful and even counterproductive.

The question “what is quality in long covid care?” is partly a philosophical one. Our findings support an approach that recognizes and values idiographic knowledge —including establishing and protecting a safe and supportive space for deliberation on individual cases to occur and to value and draw upon the collective learning that occurs in these spaces. It is through such deliberation that evidence-based guidelines can be appropriately interpreted and applied to the unique needs and circumstances of individual patients. We suggest that Drury’s warning about the limitations of nomothetic knowledge should prompt a reassessment of policies that rely too heavily on such knowledge, resulting in one-size-fits-all protocols. We also cautiously hypothesize that the need to centre the quality improvement effort on idiographic rather than nomothetic knowledge is unlikely to be unique to long covid. Indeed, such an approach may be particularly important in any condition that is complex, unpredictable, variable in presentation and clinical course, and associated with comorbidities.

Availability of data and materials

Selected qualitative data (ensuring no identifiable information) will be made available to formal research teams on reasonable request to Professor Greenhalgh at the University of Oxford, on condition that they have research ethics approval and relevant expertise. The quantitative data on NASA Lean Test have been published in full in a separate paper [ 98 ].

Abbreviations

Chronic fatigue syndrome

Intensive care unit

Jenny Ceolta-Smith

Julie Darbyshire

LOng COvid Multidisciplinary consortium Optimising Treatments and services across the NHS

Multidisciplinary team

Myalgic encephalomyelitis

Middle East Respiratory Syndrome

National Aeronautics and Space Association

Occupational therapy/ist

Post-exertional symptom exacerbation

Postural orthostatic tachycardia syndrome

Speech and language therapy

Severe Acute Respiratory Syndrome

Trisha Greenhalgh

United Kingdom

United States

World Health Organization

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Acknowledgements

We are grateful to clinic staff for allowing us to study their work and to patients for allowing us to sit in on their consultations. We also thank the funder of LOCOMOTION (National Institute for Health Research) and the patient advisory group for lived experience input.

This research is supported by National Institute for Health Research (NIHR) Long Covid Research Scheme grant (Ref COV-LT-0016).

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Trisha Greenhalgh, Julie L. Darbyshire & Emma Ladds

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Contributions

TG conceptualized the overall study, led the empirical work, supported the quality improvement meetings, conducted the ethnographic visits, led the data analysis, developed the theorization and wrote the first draft of the paper. JLD organized and led the quality improvement meetings, supported site-based researchers to collect and analyse data on their clinic, collated and summarized data on quality topics, and liaised with the patient advisory group. CL conceptualized and led the quality topic on POTS, including exploring reasons for some clinics’ reluctance to conduct testing and collating and analysing the NASA Lean Test data across all sites. EL assisted with ethnographic visits, data analysis, and theorization. JCS contributed lived experience of long covid and also clinical experience as an occupational therapist; she liaised with the wider patient advisory group, whose independent (patient-led) audit of long covid clinics informed the quality improvement prioritization exercise. All authors provided extensive feedback on drafts and contributed to discussions and refinements. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Trisha Greenhalgh .

Ethics declarations

Ethics approval and consent to participate.

LOng COvid Multidisciplinary consortium Optimising Treatments and servIces acrOss the NHS study is sponsored by the University of Leeds and approved by Yorkshire & The Humber—Bradford Leeds Research Ethics Committee (ref: 21/YH/0276) and subsequent amendments.

Patient participants in clinic were approached by the clinician (without the researcher present) and gave verbal informed consent for a clinically qualified researcher to observe the consultation. If they consented, the researcher was then invited to sit in. A written record was made in field notes of this verbal consent. It was impractical to seek consent from patients whose cases were discussed (usually with very brief clinical details) in online MDTs. Therefore, clinical case examples from MDTs presented in the paper are fictionalized cases constructed from multiple real cases and with key clinical details changed (for example, comorbidities were replaced with different conditions which would produce similar symptoms). All fictionalized cases were checked by our patient advisory group to check that they were plausible to lived experience experts.

Consent for publication

No direct patient cases are reported in this manuscript. For details of how the fictionalized cases were constructed and validated, see “Consent to participate” above.

Competing interests

TG was a member of the UK National Long Covid Task Force 2021–2023 and on the Oversight Group for the NICE Guideline on Long Covid 2021–2022. She is a member of Independent SAGE.

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Greenhalgh, T., Darbyshire, J.L., Lee, C. et al. What is quality in long covid care? Lessons from a national quality improvement collaborative and multi-site ethnography. BMC Med 22 , 159 (2024). https://doi.org/10.1186/s12916-024-03371-6

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DOI : https://doi.org/10.1186/s12916-024-03371-6

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• Post-covid-19 syndrome
• Quality improvement
• Breakthrough collaboratives
• Warranted variation
• Unwarranted variation
• Improvement science
• Ethnography
• Idiographic reasoning
• Nomothetic reasoning

BMC Medicine

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Distinct experiences and care needs of advanced cancer patients with good ECOG performance status: a qualitative phenomenological study

• Ping Chen 1 , 3   na1 ,
• Mingfu Ding 2   na1 ,
• Changlin Li 3 ,
• Yujuan Long 4 ,
• Deng Pan 5 ,
• Taiguo Liu 3 &
• Cheng Yi 1  

BMC Palliative Care volume  23 , Article number:  102 ( 2024 ) Cite this article

Metrics details

Advanced cancer patients with good Eastern Cooperative Oncology Group (ECOG) performance status (score 0–1) are underrepresented in current qualitative reports compared with their dying counterparts.

To explore the experiences and care needs of advanced cancer patients with good ECOG.

A qualitative phenomenological approach using semi-structured interview was employed. Data was analyzed using the Colaizzi’s method.

Setting/Participants

Purposive sample of terminal solid cancer patients on palliative care aged 18–70 years with a 0–1 ECOG score were recruited from a tertiary general hospital.

Sixteen participants were interviewed. Seven themes were generated from the transcripts, including experiencing no or mild symptoms; independence in self-care, decision-making, and financial capacity; prioritization of cancer growth suppression over symptom management; financial concerns; hope for prognosis and life; reluctance to discuss death and after-death arrangements; and use of complementary and alternative medicine (CAM) and religious coping.

Conclusions

Advanced cancer patients with good ECOG have distinct experiences and care needs from their dying counterparts. They tend to experience no or mild symptoms, demonstrate a strong sense of independence, and prioritize cancer suppression over symptom management. Financial concerns were common and impact their care-related decision-making. Though being hopeful for their prognosis and life, many are reluctant to discuss death and after-death arrangements. Many Chinese patients use herbal medicine as a CAM modality but need improved awareness of and accessibility to treatment options. Healthcare professionals and policy-makers should recognize their unique experiences and needs when tailoring care strategies and policies.

Key statements

What is already known about the topic?

• Even in their advanced stage, cancer patients with good ECOG performance status are capable of self-care and less reliant on care provided by other.

• Existing qualitative research mainly focuses on advanced cancer patients with poor ECOG, emphasizing pain management, emotional distress, and palliative care.

What this paper adds?

• Our findings reveal distinct experiences and care needs of advanced cancer patients with good ECOG performance status from their dying counterparts.

Implications for practice, theory or policy .

• Healthcare professionals should recognize and address the patient group’s distinct needs.

• Future research should further investigate their symptom trajectory, influencing factors, and care needs to fill the gap in their cancer journey.

• Policy-makers should develop tailored policies that consider good ECOG performance status.

Peer Review reports

Introduction

Cancer is a major global public health issue, which affects millions of people around the world and in China. There were estimated 19.3 million new cancer cases and 10 million cancer deaths globally in 2020 [ 1 ]. China accounted for 24% of newly diagnosed cases and 30% of the cancer-related deaths [ 2 ]. As treatments and early detection methods continue to advance, the number of people living with cancer are on the rise, whose life expectancy is extended. For instance, the five-year survival rate for cancer patients in China rose from 30.9% in 2003–2005 to 40.5% in 2012–2015 [ 3 ], highlighting the growing population of individuals living with cancer for extended periods.

Cancer staging is crucial in clinical oncology. It provides pivotal information on the extent and spread of the disease to guide treatment decisions and prognosis [ 4 ]. Advanced-stage cancer patients, typically characterized by extensive tumor growth and metastasis, often face more aggressive treatments and poorer prognoses compared with those in an earlier stage. However, advanced cancer patients with good physical performance status, commonly measured as an Eastern Cooperative Oncology Group performance status (ECOG PS) score, are often underrepresented in the current qualitative research. Instead, their counterparts with poorer ECOG PS who are usually in the final stage of disease are the focus of current research interests, who typically experience more severe symptoms, functional decline, and higher risks of adverse events [ 5 , 6 ]. Researchers may opt not to recruit patients with good ECOG PS, who are more likely to continue working and engaging in daily activities and may be less accessible [ 7 ]. Also, this patient population may be neglected in favor of cancer patients nearing death because of limited research funding and resources [ 8 ].

In our clinical practice of palliative and hospice care, we often encounter cancer patients in the advanced stage with good ECOG PS (score 0–1). They impress us by demonstrating distinct physical and psychological experiences as well as varied care needs from those with poorer PS. This led us to hypothesize that despite the advanced stage of disease, cancer patients with good ECOG PS may have different experiences and care needs from their dying counterparts. Therefore, we devised this qualitative phenomenological study, which aimed to explore the experiences and care needs of advanced cancer patients with good ECOG PS. Our findings may shed light on some distinct characteristics of this underrepresented patient group and reveal novel areas potentially meaningful for future research. The evidence may find merit in supporting care-providers and decision-makers to more specifically tailor their strategies when caring for cancer patients in the advanced stage.

Study design

This was a phenomenological qualitative study. Semi-structured, in-depth interviews were conducted face-to-face with eligible participants to gather information about their lived experiences and care needs. The interviews continued until data saturation was achieved [ 9 ], at which point no new insights emerged from further interviews. Colaizzi’s method was employed to analyze the collected data [ 10 ].

Ethical consideration and informed consent

The study was ethically approved by the Ethics Committee of Chengdu Seventh People’s Hospital (reference number AF-SOP-09-2.1). Written informed consents were signed with all participants.

The present study took place at the Oncology Department of Chengdu Seventh People’s Hospital, a tertiary general hospital situated in Chengdu, Sichuan Province, Southwest China. As one of the national pilot centers for palliative and hospice care, this department offers both curative cancer treatments and palliative or hospice care to cancer patients across all stages of tumor progression.

Research team

Our research team consisted of 8 members, including 6 oncologists and 2 nurses. Each researcher had a minimum of 5 years of clinical experience in oncology, with at least 2 years in palliative care. Additionally, all team members were trained in phenomenological qualitative methodology. None of the researchers held religious affiliations or had any known inclined theoretical or ethical perspectives. The principal investigator underwent training in face-to-face semi-structured interview.

Participants

Participants were recruited from patients who received treatment and/or follow-up care at the Oncology Department between July and September 2022. The principal investigator screened potential participants based on the following inclusion and exclusion criteria:

Inclusion criteria: A patient should (1) be 18–70 yrs old with a stage IV cancer diagnosis of solid tumor and a life expectancy > 6 mo; (2) have an ECOG PS score of 0–1; (3) be currently undergoing palliative therapies, including but not limited to palliative chemotherapy and radiotherapy; (4) be aware of their diagnosis; (5) be able to participate in a 30 min in-depth interview; (6) possess sufficient cognitive capacity and verbal communication ability; and (7) provide consent to participate in the study and for the publication of findings.

Exclusion criteria: A patient was ineligible if they (1) were < 18 or > 70 yrs old; (2) had a non-cancer or non-solid tumor diagnosis, a tumor stage below IV, an ECOG PS score > 1, or a life expectancy < 6 mo; (3) were unaware of their diagnosis; (4) were receiving hospice or end-of-life care; (5) were physically unfit for an interview, as assessed by the interviewer; (6) had a known mental illness or exhibited insufficient cognitive and communication capacity; or (7) failed to provide consent.

Sampling and data collection

Purposive sampling was employed to select participants. The primary investigator accessed candidates’ medical records on the hospital’s electronic medical record system, screening them against the inclusion and exclusion criteria. She then approached a potential participant and made a brief casual conversation to visually assess their physical and mental status, cognitive abilities, and communication skills.

If the candidate seemed suitable for further interview, the primary investigator explained the study’s objectives and process, and inquired if they were interested in participating. After obtaining informed consent, the primary investigator either initiated the interview immediately or scheduled it for a later time, typically within the next 72 h. For those with scheduled interviews, the interviewer reassessed the participant’s status at the time of the interview. If the participant was deemed unfit by the primary investigator, the interview would be canceled and the participant excluded.

Participants were interviewed individually in a designated room, without the presence of family members. If a participant displayed signs of physical discomfort or reluctance to continue, the interview would be discontinued and any incomplete interviews discarded.

The sampling process persisted until data saturation was reached [ 9 ], at which point the interviews no longer produced new analytical information.

Semi-structured interview

The primary investigator conducted all semi-structured interviews to ensure consistency across the study. To prevent interviewer burnout and allow for timely verbatim transcription, no more than three participants were interviewed on a single day. Each interview was audio-recorded in its entirety.

Before delving into specific questions, the interviewer posed a grand tour question to guide the participant, typically phrased as “How do you feel today?” or “Could you tell me about how you feel recently?” Probing questions such as “Could you tell me more about the care/doctors/nurses?“, “Has the therapy made you feel better?“, and “What makes you think that?” were utilized to encourage participants to provide more detailed responses. The interviewer employed various techniques, such as rhetorical questioning, repetition, and response, to uncover the participants’ genuine feelings. Field notes were taken throughout the interviews to document the participants’ tone of voice, notable facial expressions, and body gestures. (Supplement 1 )

Immediately following each interview, the audio recordings and notes were cataloged. Another investigator transcribed the recordings verbatim and verified the accuracy of transcriptions within 24 h after the interview. To ensure anonymity, the records and transcriptions were de-identified, with participants assigned numerical identifiers (P1, P2, etc.) in place of personal information. All patient data, recordings, and transcriptions were maintained with strict confidentiality.

To ensure the rigor of this study, several strategies were employed. The interviewer maintained neutrality throughout the interviews by refraining from expressing personal opinions or judgments. When unclear statements or feelings arose, the primary investigator sought clarification from the participants during the interview. In cases of disagreement among researchers, the team referred back to the interview transcriptions and, if necessary, sought further clarification from the participants.

Participants and interviews

In this study, we interviewed 16 participants aged 33–64 yrs (mean age, 55.1 yrs), including 7 females. The interviews had an average duration of 17.7 min (range, 10.5–42.5 min). All participants were medically insured and diagnosed with stage IV solid tumors. Half of the participants (8/16) were asymptomatic, while the remaining experienced mild symptoms. Among the participants, 15 expressed expectations with their current treatment to suppress tumor growth, and 4 of symptomatic patients expected it to alleviate symptoms. Nine participants reported using Chinese herbal medicine (CHM) as a complement to their ongoing treatment. Two participants reported holding religious beliefs, both identifying as Christians. Detailed sociodemographic and clinical information of the participants is in Tables  1 and 2 .

Findings from interviews

We extracted seven overarching themes from the interviews, as follows:

Theme 1: experiencing no or mild symptoms

The majority of participants reported experiencing either no symptoms or only mild symptoms during the interview period, which did not substantially impact their daily lives. However, a few participants recounted instances of severe symptoms, such as debilitating pain, that hindered their ability to engage in routine daily activities.

“(I) feel fine. Almost nothing. Just a little coughing now and then.” - P7 .

“No, I feel not bad… (I) only feel more tired than before every day. Nothing else.” - P8 .

“My shoulder used to hurt really badly. I couldn’t lift my right arm… It hurt so badly that I’d rather die… Couldn’t do anything… (The pain) was relieved again after ascites extraction. Now it hurts a little some times but OK.” - P11 .

The most severe symptom was described by Participant 16, who had experienced abdominal distention, which was managed by ascites extraction:

“My belly felt very full all the time… Better after the ascites was extracted. (I) couldn’t do anything then but can take care of myself again now, at least do some of my own things now.” (Smiled) - P16 .

Theme 2: independence in self-care, decision-making, and financial capacity

The participants exhibited a strong sense of independence in multiple aspects, including self-care, care-related decision-making, and financial capability for care expenses.

Rather than being heavily dependent on family members or other informal caregivers, the majority of participants were either fully or partially self-sufficient in managing their daily living activities:

“I take care of myself. My husband helps sometimes especially when I’m hospitalized but I don’t like his cooking.” - P1 .

“I can take care of myself. They (family members) are busy working every day. I cook my meals. Easier for me to choose what I want to eat.” - P4 .

“My wife looks after me and I try to take care of my own daily living as long as I feel good enough.” - P10 .

Most participants seemed resolute in making their own decisions while having little trouble seeking input from a variety of sources:

“… I make my own decisions… My nephew works for a pharmaceutical company… He suggests me to ask if I can test only some of the gene sequencing tests…” - P1 .

In some cases, they even exhibited resistance to external interference with their decision-making, especial concerning their treatment-related decisions:

“This is my life. (I) should think for myself, whether (I) continue my treatment or stop.” - P3 .

Some participants mentioned that they paid for their treatments with their own savings and were hesitant to spend money from other family members or borrowed money for fear of becoming a burden:

“For now (I) still have enough money (for the treatments). (My medical) insurance reimburse most of the expense… more than 90% reimbursed. I only pay less than 10%.” - P5 .

“I pay for my own treatments… (I) don’t want him (his son) to spend his money. He’s not even married yet.” - P16 .

Some did not even quit working:

“I go back to work sometimes but they (superiors at work) don’t require me. Most of the time I work at home. Only some easy assignments… They pay me the minimal salary… I understand. It’s not easy for them either. Already very kind of them to keep me like this.” - P15 .

Theme 3: prioritization of cancer growth suppression over symptom management

Notably, when asked about their primary expectations with their current care, 15 out of 16 participants expressed a desire for cancer growth suppression. This included all 8 asymptomatic participants and 4 of the mildly symptomatic patients:

“I’ll take the PD-1 treatment (a targeted therapy) if my gene sequencing can find some new biomarker.” - P6 .

“… to control its (tumor) growth. I don’t know how much longer… There might be a new drug or treatment. Who knows.” (Laughed) - P7 .

Only 4 symptomatic participants mentioned alleviating their symptoms:

“My shoulder hurt really badly… After the ascites extraction, it didn’t hurt any more. It came back later and was relieved again after ascites extraction. Now it hurts a little some times but OK.” - P1 .

Theme 4: financial concerns

During the interviews, all participants mentioned financial concerns or burdens at some point, in some cases without any prompting from the interviewer. This was frequently associated with their decision to discontinue their current treatment. Notably, “running out of money” was identified as the primary reason for discontinuing treatment, which was emphasized more strongly than “not respond to therapy”:

“I’ll go on with the therapies as long as (I) have money… My son told me just to keep getting treated. He borrowed 8,000 Yuan when (he) came back from Shanghai. But I don’t want him to borrow money… I don’t want to become a burden (for my family). (I) just stop my treatment when I run out of money.” - P4 .

“… I’ll go on (with the treatment) as long as I can. Have to discontinue if no more money. What else can I do?” - P6 .

The availability of medical insurance reimbursement played a critical role in enabling them to afford ongoing treatment:

“The disease is a heavy burden. I can still afford it now. My medical insurance reimburses most of it but still a heavy burden. I’ll just discontinue therapy when run out of money.” - P2 .

Theme 5: hope for prognosis and life

Despite being aware of their diagnoses, the participants demonstrated a sense of hope for the prognosis of their treatments and their future prospects in life:

“I have no regret in life. My only unfinished business is to see my son getting married. I want to live to see it.” - P1 .

“The targeted therapy worked very well for me. I’m still taking it. Hopefully, it will last long.” - P9 .

Instead of giving up, they tended to seek new potential treatments if a particular therapy had failed:

“The doctor told me to take gene sequencing and see if some new biomarkers can be found after I stopped responding to the last medicine. I did and am waiting for the results now. (I) hope they can find a new one… Is there any other treatment I can use?” - P2 .

“… (the tumor) grew and spread again after my last surgery. The doctor said that more surgeries may not do any more good but there are still other possibilities, like targeted therapy.” - P7 .

Interestingly, none of the participants brought up topics such as the desire for dying with dignity or the consideration of a Do-Not-Resuscitate (DNR) agreement.

Theme 6: reluctance to discuss death and after-death arrangements

Two specific questions were incorporated into the interviews to inquire whether participants had contemplated their own death and discussed after-death arrangements with their families. The responses were varied. Nearly half of the participants (7/16) reported that they had either never or rarely thought about death, nor had they discussed after-death arrangements with their family members:

“I have never thought about it (death). Don’t want to… still a little afraid to talk about it.” - P5 .

“It crosses my mind sometimes but I don’t think about it… (I) have never discussed (the after-death arrangements) with them (family). It’s not time yet.” - P8 .

In contrast, some other participants seemed open to think about and discuss them:

“I did. That’s fine. I know I have it (cancer). They know I have it too. There’s no need to hide or fear. It’s pointless. Doesn’t help with anything. I talked about it (my death) with them (my family) once. They were kinda shy and almost cried. Then I stopped.” (Laughed) - P12 .

“(I) discussed my after-death arrangements with my family already. Better to get prepared earlier. No one knows when the time will come.” - P13 .

Theme 7: use of complementary and alternative medicine and religious coping

Seven participants reported utilizing complementary and alternative medicine (CAM) modalities, which were primarily limited to Chinese herbal medicine, acupuncture, and massage. The main reasons for use of CAM were to assist in suppressing tumor growth and alleviating symptoms:

“I took Chinese herbal medicine after chemotherapy to help with my nausea and vomiting. I also had acupuncture for my headache.” - P2 .

“I went to see a traditional Chinese medicine doctor. He was famous for treating cancer with herbal medicine.” - P9 .

An interesting observation was that two participants cited engaging in religious activities as part of their coping strategies. Notably, one of them adopted her religious practices shortly after receiving her cancer diagnosis:

“I began to believing in Christianity two years again… one month after I was diagnosed. The sisters (fellow believers) said that they would pray for me to heal… I enjoy the peace and joy.” - P2 .

“Yes, I’m a Christian… I pray for my health and healing sometimes.” - P16 .

Main findings

On this qualitative study, we sought to explore the experiences and care needs of advanced cancer patients with good ECOG PS, a population that has not been extensively studied. Understanding their unique perspectives is vital for optimizing care and ensuring that the patients’ needs are met in the continuum of cancer care. We were able to extract seven overarching themes from the narratives of our in-depth interviews with 16 participants, including their symptoms, sense of independence, treatment priorities, financial concerns, hope, reluctance to discuss death, and use of complementary and alternative medicine.

In the current study, we found most of our participants either asymptomatic or mildly symptomatic. This is consistent with previous studies about the relationship between performance status, symptom burden, and disease stage. The ECOG PS is widely used and essential measure of the functional capacity of cancer patient, which is shown to correlate with clinical outcomes, treatment tolerance, and survival [ 11 ]. Patients with good ECOG PS generally have no or limited disease-related restrictions, despite the advanced cancer stage. According to Cormier et al., several factors may contribute to their contrast with the poor PS patients, such as better overall health, effective symptom management, and more favorable tumor characteristics or response to treatment [ 12 ]. This practically differentiates the two sub-groups of advanced cancer patients not only in terms of their experiences of symptoms but their distinct care needs. Compared with their counterparts with poor PS, the better performing patients need less symptom management and psychological support and may have different care priorities, which echoes with our finding that the participants prioritized tumor suppression therapies over symptom management. It is important for care-providers to recognize the variability in symptom burden due to varied ECOG PS, monitor the symptom trajectory of a patient, and devise care strategies accordingly.

The second theme highlights the participants’ strong sense of independence in various aspects of their cancer journey, including self-care, care-related decision-making, and financial capacity. Their good ECOG PS may enable them to maintain functional autonomy and actively engage in daily activities and decision-making processes [ 11 ], who are either fully or partially self-sufficient in managing their daily living activities. This finding is significant as advanced cancer patients with good PS may require less assistance from family members and other informal caregivers. This autonomy in self-care can contribute to their overall quality of life and psychological well-being because maintaining independence is often considered essential in coping with cancer [ 13 ]. The participants’ resoluteness in making their own decisions while seeking input from various sources demonstrates their active engagement in their care process, which may lead to better treatment outcomes and satisfaction with care, as patients who participate in their care decisions often report feeling more empowered and in control of their lives [ 14 ]. It is vital for healthcare professionals to respect and support the patients’ autonomy by providing the necessary information and guidance for them to make informed choices about care [ 15 ]. Another aspect of independence was their financial capacity. According to Lentz and colleagues, having the financial resources to pay for care can alleviate some of the stress and burden associated with managing cancer and its treatments [ 16 ], which can further contribute to a patient’s sense of control and well-being during their care journey.

It is worth noting, however, that the participants in this study may not be representative of the broader population of advanced cancer patients because they were all medically insured, which covered a significant portion of their medical expenses. This reduced their out-of-pocket costs significantly, contributing to their sense of financial independence. The potential selection bias in our sample should be considered when interpreting our findings. The experiences of participants with medical insurance might not accurately reflect those without such coverage. Uninsured or under-insured patients may face substantial financial burden and stress relating to the costs of care [ 16 ]. Furthermore, financial ability to pay for care can play a crucial role in a patient’s decision-making, particularly when it comes to deciding whether to continue or discontinue treatment. Patients who are financially constrained may be more likely to consider discontinuing treatment, even if they are clinically eligible for and may benefit from the treatment [ 16 ], which was evident in our study. The financial burden of cancer treatment can lead to significant distress, prompting patients to weigh the benefits of treatment against the costs [ 17 ]. This may result in decisions not entirely aligned with their medical needs and preferences and potentially compromise their quality of care and clinical outcomes [ 18 ]. It is imperative that healthcare professionals should be attentive to a patient’s financial concerns, even when they perform well physically, and engage in open discussions about the costs of care and potential resources to support their decision-making.

The finding that advanced cancer patients with good ECOG PS prioritize cancer suppression therapies over symptom management was expectable. This preference can be easily understood according to Maslow’s hierarchy of needs where one would strive to fulfill their basic needs before addressing higher-order needs [ 19 ]. In our context, symptom management can be considered a basic need, as it addresses a patient’s physiological and safety concerns. Once the basic need is met, patients tend to shift their focus to higher-order needs, such as achieving the best possible cancer control and prolonging survival. In our case, the participants with good ECOG PS are mostly asymptomatic or mildly symptomatic. Their basic needs in terms of symptom management are relatively well-addressed. Therefore, they are more likely to prioritize such higher-order needs as cancer suppression for better clinical outcomes and maintain their functional status. This finding has significant implications for improving the prognosis of advanced cancer patients with good ECOG PS, who are generally more functional and experience fewer severe symptoms and as a result may be physically more tolerant to therapies, including aggressive or experimental treatments. This increased tolerance can enable clinicians to consider a broader range of treatment options, which may lead to better cancer control and improved their clinical outcomes, including better symptom management and prolonged survival. Additionally, these patients seem more driven to explore new and experimental treatment options. Their willingness to participate in clinical trials or seek innovative therapies may provide them with access to cutting-edge treatments with potentially benefits for their prognosis and survival [ 20 ]. As healthcare professionals, we should recognize this motivation and support them by providing information on clinical trials or innovative therapies.

Hope is known to have significant implications for advanced cancer patients, which may influences their emotional well-being, treatment decisions, and overall quality of life [ 21 , 22 , 23 , 24 ]. We found that the patients with good ECOG PS demonstrate hope for their prognosis and future prospects of life. Though being hopeful is generally beneficial for advanced cancer patients and may reinforce patient’s motivation to seek care positively, it must be noted that it can sometimes lead to unrealistic expectations or misguided decision-making [ 25 ]. This is particularly relevant in advanced cancer patients with good ECOG PS because their hopefulness and desire for aggressive treatment may lead them to misjudge their physical status and pursue therapies with limited efficacy or significant side effects [ 26 , 27 ]. It becomes crucial for healthcare professionals to be aware of this potential pitfall and ensure that patients are properly informed about their prognosis, treatment options, and potential risks and benefits.

It was noteworthy that none of our participants brought up such topics as the desire for dying with dignity or the consideration of a DNR agreement. Their relatively better quality of life and sense of hopefulness could make them more focused on treatment and improving prognosis, rather than considering end-of-life decisions [ 23 , 24 ]. Besides fear for death, another possible attributing factor is culture. Death and after-death arrangements are commonly thought of as negative topics or even taboos in Chinese culture, which are usually avoided especially when an individual is still living [ 28 ]. This resonates with Theme 6 where many participants were found reluctant to contemplate their own death and discuss after-death arrangements with their families. Their needs to be prepared for death, seek dignity in passing, and make arrangements after their deaths are expected to increase as they near the end of life [ 29 ]. Healthcare professionals should monitor their disease progression closely and offer support when needed.

Almost half of our participants (7/16) reported using CAM modalities, which were primarily limited to Chinese herbal medicine, acupuncture, and massage. The main reasons for employing CAM were to aid in suppressing tumor growth and alleviating symptoms. This finding highlights the diverse approaches that patients may take in searching for effective treatments, particularly where conventional therapies have limited efficacy or are associated with substantial side effects [ 11 ]. The limited diversity of CAM modalities suggests that the patients may lack awareness or accessibility to a broader array of CAM options, which is consistent with previous reports [ 30 , 31 ]. Furthermore, despite the clinical trials to investigate the safety and efficacy of Chinese herbal medicine as a complement to mainstream cancer treatments, there is still insufficient evidence to establish CAM modalities for suppressing tumor growth. As a result, use of CAM should be approached with caution and mainly considered for purposes other than tumor treatment.

It is intriguing to find that two of the participants engaged in religious activities as part of their coping strategies because Chinese people are often believed to have a lower prevalence of religious beliefs and studies investigating religious coping in the Chinese population are scarce [ 32 ]. A main impression of the two religious patients was that they seemed to use religion for practical purposes, rather than fully embracing the beliefs. This is consistent with previous studies where cancer patients may seek out religious practices as a way to manage stress, find peace, and maintain a sense of hope during difficult times. Praying for healing, for example, can provide a sense of control and agency in a situation where they may feel powerless [ 33 ]. Future research may continue to pursue the subject among Chinese cancer patients.

Compared with dying advanced cancer patients with poor ECOG PS, those with good ECOG PS display distinct experiences and care needs. They generally have milder symptoms, higher independence in self-care, decision-making, and financial capacity, and prioritize tumor suppression therapies over symptom management. On the other hand, patients with poor ECOG PS grapple with a higher symptom burden, increased reliance on support, and a focus on symptom relief and palliative care. Though sharing concerns about finances, demonstrating hopefulness, utilizing CAM, the two groups have varying degrees and objectives. Recognizing these distinctions is essential for healthcare professionals to provide customized, patient-centered care to address the unique needs of the well-performing cancer patients.

Strengths and limitations

As one of the few qualitative investigations to explore the experiences and care needs of advanced cancer patients with good ECOG PS, our study revealed that this patient group is distinct from their dying counterparts, who are the focus of the current research literature. Our findings highlight some observations characteristic of the patient group such as their asymptomatic or mildly symptomatic experiences, stronger sense of independence in various perspectives, and distinct care prioritization from those nearing death. However, further quantitative studies are needed to determine the association and synergistic dynamics of such characteristics.

Implications for practice

It is vital for healthcare professionals and policy-makers to recognize these unique experiences and care needs of advanced cancer patients with good ECOG PS and respond by providing necessary information, education, treatment options, and care strategies.

Data availability

Due to the sensitive nature of the interview recordings, the original audio recordings are prohibited from sharing. The desensitized transcripts and demographic data are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the Cancer Psychology and Health Management Committee of Sichuan Cancer Society (S.C.S.) for their exceptional guidance.

This work was supported by Medical Research Project of Sichuan Medical Association (S20061) and the Joint Research Fund of Chengdu Medical College-Chengdu Seventh People’s Hospital (2020LHJYZD-03). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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Ping Chen and Mingfu Ding contributed equally to this work.

Authors and Affiliations

Abdominal Oncology Ward, Division of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, 610041, Sichuan Province, China

Ping Chen & Cheng Yi

Rehabilitation Medicine Department, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, 610041, Sichuan Province, China

Mingfu Ding

Department of Oncology, Chengdu Seventh People’s Hospital (Affiliated Cancer Hospital of Chengdu Medical College), No. 1 Shi’er Zhong Street, Wuhou District, Chengdu, 610041, Sichuan Province, China

Ping Chen, Changlin Li, Li Ma & Taiguo Liu

Department of Intensive Care Unit, Chengdu Seventh People’s Hospital (Affiliated Cancer Hospital of Chengdu Medical College), No. 1 Shi’er Zhong Street, Wuhou District, Chengdu, 610041, Sichuan Province, China

Yujuan Long

Department of Medical Oncology, Guangxi Academy of Medical Sciences and the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, 530016, Guangxi, China

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PC is the primary investigator and conducted the interviews. PC, MD, TL, and CY conceptualised and designed this study. PC, CL, and YL processed the data and verified accuracy of data against the transcripts. PC, MD, CL, YL, DP, LM, TL, and CY analyzed the data and interpreted the findings. PC, TL, and CY wrote the initial draft. MD, CL, and YL provided critical feedback about the draft. All authors reviewed and approved the final manuscript for submission.

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Correspondence to Taiguo Liu or Cheng Yi .

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This study was performed according to the appropriate Chinese laws and regulations, the principles outlined in the Declaration of Helsinki, and Good Clinical Practice guidelines. The study was ethically approved by the Ethics Committee of Chengdu Seventh People’s Hospital (reference number AF-SOP-09-2.1). Written informed consents were signed with all participants.

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Chen, P., Ding, M., Li, C. et al. Distinct experiences and care needs of advanced cancer patients with good ECOG performance status: a qualitative phenomenological study. BMC Palliat Care 23 , 102 (2024). https://doi.org/10.1186/s12904-024-01425-3

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Corporate activities that influence population health: A scoping review and qualitative synthesis to develop the HEALTH-CORP typology

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Introduction The concept of the commercial determinants of health (CDH) is used to study the actions (and associated structures) of commercial entities that influence population health and health equity. The aim of this study was to develop a typology that describes the diverse set of activities through which corporations influence population health and health equity across industries.

Methods We conducted a scoping review of articles using CDH terms (n=116) that discuss corporate activities that can influence population health and health equity across 16 industries. We used the qualitative constant comparison method to build a typology called the Corporate Influences on Population Health (HEALTH-CORP) typology.

Results The HEALTH-CORP typology identifies 70 corporate activities that can influence health across industries and categorizes them into seven domains of corporate influence (e.g., political practices, employment practices). We present a model that situates these domains based on their proximity to health outcomes and identify five population groups (e.g., workers, local communities) to consider when evaluating corporate health impacts.

Discussion The HEALTH-CORP typology facilitates an understanding of the diverse set of corporate activities that can influence population health and the population groups affected by these activities. We discuss the utility of these contributions in terms of identifying interventions to address the CDH and advancing efforts to measure and monitor the CDH. We also leverage our findings to identify key gaps in CDH literature and suggest avenues for future research.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Raquel Burgess was supported by a Doctoral Foreign Study Award provided by the Canadian Institutes of Health Research at the time this research was conducted. Funding was provided by the Yale School of Public Health and the Yale Graduate Student Assembly to present this work at the American Public Health Association Annual Meeting in 2022.

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I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

The data for this study are published academic articles which are available from the respective publishers (see Supplementary Material, Appendix 2 for the characteristics of included articles). In addition, we uploaded the following files to Open Science Framework (DOI 10.17605/OSF.IO/TG9S7) to support data availability: 1) a .csv file containing a list of the articles that underwent title and abstract screening in our study and the respective screening decisions that were assigned, and 2) .ris files containing the citations to the respective articles and the assigned screening decisions, which can be uploaded into a reference manager. Interested parties can contact the corresponding author for additional information.

https://osf.io/tg9s7/

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Research Frameworks: Critical Components for Reporting Qualitative Health Care Research

Qualitative health care research can provide insights into health care practices that quantitative studies cannot. However, the potential of qualitative research to improve health care is undermined by reporting that does not explain or justify the research questions and design. The vital role of research frameworks for designing and conducting quality research is widely accepted, but despite many articles and books on the topic, confusion persists about what constitutes an adequate underpinning framework, what to call it, and how to use one. This editorial clarifies some of the terminology and reinforces why research frameworks are essential for good-quality reporting of all research, especially qualitative research.

Qualitative research provides valuable insights into health care interactions and decision-making processes – for example, why and how a clinician may ignore prevailing evidence and continue making clinical decisions the way they always have. 1 The perception of qualitative health care research has improved since a 2016 article by Greenhalgh et al. highlighted the higher contributions and citation rates of qualitative research than those of contemporaneous quantitative research. 2 The Greenhalgh et al. article was subsequently supported by an open letter from 76 senior academics spanning 11 countries to the editors of the British Medical Journal . 3 Despite greater recognition and acceptance, qualitative research continues to have an “uneasy relationship with theory,” 4 which contributes to poor reporting.

As an editor for the Journal of Patient-Centered Research and Reviews , as well as Human Resources for Health , I have seen several exemplary qualitative articles with clear and coherent reporting. On the other hand, I have often been concerned by a lack of rigorous reporting, which may reflect and reinforce the outdated perception of qualitative research as the “soft option.” 5 Qualitative research is more than conducting a few semi-structured interviews, transcribing the audio recordings verbatim, coding the transcripts, and developing and reporting themes, including a few quotes. Qualitative research that benefits health care is time-consuming and labor-intensive, requires robust design, and is rooted in theory, along with comprehensive reporting. 6

What Is “Theory”?

So fundamental is theory to qualitative research that I initially toyed with titling this editorial, “ Theory: the missing link in qualitative health care research articles ,” before deeming that focus too broad. As far back as 1967, Merton 6 warned that “the word theory threatens to become meaningless.” While it cannot be overstated that “atheoretical” studies lack the underlying logic that justifies researchers’ design choices, the word theory is so overused that it is difficult to understand what constitutes an adequate theoretical foundation and what to call it.

Theory, as used in the term theoretical foundation , refers to the existing body of knowledge. 7 , 8 The existing body of knowledge consists of more than formal theories , with their explanatory and predictive characteristics, so theory implies more than just theories . Box 1 9 – 12 defines the “building blocks of formal theories.” 9 Theorizing or theory-building starts with concepts at the most concrete, experiential level, becoming progressively more abstract until a higher-level theory is developed that explains the relationships between the building blocks. 9 Grand theories are broad, representing the most abstract level of theorizing. Middle-range and explanatory theories are progressively less abstract, more specific to particular phenomena or cases (middle-range) or variables (explanatory), and testable.

The Building Blocks of Formal Theories 9

The importance of research frameworks.

Researchers may draw on several elements to frame their research. Generally, a framework is regarded as “a set of ideas that you use when you are forming your decisions and judgements” 13 or “a system of rules, ideas, or beliefs that is used to plan or decide something.” 14 Research frameworks may consist of a single formal theory or part thereof, any combination of several theories or relevant constructs from different theories, models (as simplified representations of formal theories), concepts from the literature and researchers’ experiences.

Although Merriam 15 was of the view that every study has a framework, whether explicit or not, there are advantages to using an explicit framework. Research frameworks map “the territory being investigated,” 8 thus helping researchers to be explicit about what informed their research design, from developing research questions and choosing appropriate methods to data analysis and interpretation. Using a framework makes research findings more meaningful 12 and promotes generalizability by situating the study and interpreting data in more general terms than the study itself. 16

Theoretical and Conceptual Frameworks

The variation in how the terms theoretical and conceptual frameworks are used may be confusing. Some researchers refer to only theoretical frameworks 17 , 18 or conceptual frameworks, 19 – 21 while others use the terms interchangeably. 7 Other researchers distinguish between the two. For example, Miles, Huberman & Saldana 8 see theoretical frameworks as based on formal theories and conceptual frameworks derived inductively from locally relevant concepts and variables, although they may include theoretical aspects. Conversely, some researchers believe that theoretical frameworks include formal theories and concepts. 18 Others argue that any differences between the two types of frameworks are semantic and, instead, emphasize using a research framework to provide coherence across the research questions, methods and interpretation of the results, irrespective of what that framework is called.

Like Ravitch and Riggan, 22 I regard conceptual frameworks (CFs) as the broader term. Including researchers’ perspectives and experiences in CFs provides valuable sources of originality. Novel perspectives guard against research repeating what has already been stated. 23 The term theoretical framework (TF) may be appropriate where formal published and identifiable theories or parts of such theories are used. 24 However, existing formal theories alone may not provide the current state of relevant concepts essential to understanding the motivation for and logic underlying a study. Some researchers may argue that relevant concepts may be covered in the literature review, but what is the point of literature reviews and prior findings unless authors connect them to the research questions and design? Indeed, Sutton & Straw 25 exclude literature reviews and lists of prior findings as an adequate foundation for a study, along with individual lists of variables or constructs (even when the constructs are defined), predictions or hypotheses, and diagrams that do not propose relationships. One or more of these aspects could be used in a research framework (eg, in a TF), and the literature review could (and should) focus on the theories or parts of theories (constructs), offer some critique of the theory and point out how they intend to use the theory. This would be more meaningful than merely describing the theory as the “background” to the study, without explicitly stating why and how it is being used. Similarly, a CF may include a discussion of the theories being used (basically, a TF) and a literature review of the current understanding of any relevant concepts that are not regarded as formal theory.

It may be helpful for authors to specify whether they are using a theoretical or a conceptual framework, but more importantly, authors should make explicit how they constructed and used their research framework. Some studies start with research frameworks of one type and end up with another type, 8 , 22 underscoring the need for authors to clarify the type of framework used and how it informed their research. Accepting the sheer complexity surrounding research frameworks and lamenting the difficulty of reducing the confusion around these terms, Box 2 26 – 31 and Box 3 offer examples highlighting the fundamental elements of theoretical and conceptual frameworks while acknowledging that they share a common purpose.

Examples of How Theoretical Frameworks May Be Used

Examples of how conceptual frameworks may be used, misconceptions about qualitative research.

Qualitative research’s “uneasy relationship with theory” 4 may be due to several misconceptions. One possible misconception is that qualitative research aims to build theory and thus does not need theoretical grounding. The reality is that all qualitative research methods, not just Grounded Theory studies focused on theory building, may lead to theory construction. 16 Similarly, all types of qualitative research, including Grounded Theory studies, should be guided by research frameworks. 16

Not using a research framework may also be due to misconceptions that qualitative research aims to understand people’s perspectives and experiences without examining them from a particular theoretical perspective or that theoretical foundations may influence researchers’ interpretations of participants’ meanings. In fact, in the same way that participants’ meanings vary, qualitative researchers’ interpretations (as opposed to descriptions) of participants’ meaning-making will differ. 32 , 33 Research frameworks thus provide a frame of reference for “making sense of the data.” 34

Studies informed by well-defined research frameworks can make a world of difference in alleviating misconceptions. Good qualitative reporting requires research frameworks that make explicit the combination of relevant theories, theoretical constructs and concepts that will permeate every aspect of the research. Irrespective of the term used, research frameworks are critical components of reporting not only qualitative but also all types of research.

Acknowledgments

In memory of Martie Sanders: supervisor, mentor, and colleague. My deepest gratitude for your unfailing support and guidance. I feel your loss.

Conflicts of Interest: None.