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• How to Do Thematic Analysis | Guide & Examples

How to Do Thematic Analysis | Guide & Examples

Published on 5 May 2022 by Jack Caulfield .

Thematic analysis is a method of analysing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes, topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process:

• Familiarisation
• Generating themes
• Reviewing themes
• Defining and naming themes

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarisation, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences, or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

• How do patients perceive doctors in a hospital setting?
• What are young women’s experiences on dating sites?
• What are non-experts’ ideas and opinions about climate change?
• How is gender constructed in secondary school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyse it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large datasets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

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Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

• An inductive approach involves allowing the data to determine your themes.
• A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

There’s also the distinction between a semantic and a latent approach:

• A semantic approach involves analysing the explicit content of the data.
• A latent approach involves reading into the subtext and assumptions underlying the data.

After you’ve decided thematic analysis is the right method for analysing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analysing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or ‘codes’ to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colours corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a condensed overview of the main points and common meanings that recur throughout the data.

Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code ‘uncertainty’ made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the dataset and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them, or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that ‘changing terminology’ fits better under the ‘uncertainty’ theme than under ‘distrust of experts’, since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at ‘distrust of experts’ and determine exactly who we mean by ‘experts’ in this theme. We might decide that a better name for the theme is ‘distrust of authority’ or ‘conspiracy thinking’.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims, and approach.

We should also include a methodology section, describing how we collected the data (e.g., through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

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What (Exactly) Is Thematic Analysis?

Plain-Language Explanation & Definition (With Examples)

By: Jenna Crosley (PhD). Expert Reviewed By: Dr Eunice Rautenbach | April 2021

Thematic analysis is one of the most popular qualitative analysis techniques we see students opting for at Grad Coach – and for good reason. Despite its relative simplicity, thematic analysis can be a very powerful analysis technique when used correctly. In this post, we’ll unpack thematic analysis using plain language (and loads of examples) so that you can conquer your analysis with confidence.

Thematic Analysis 101

• Basic terminology relating to thematic analysis
• What is thematic analysis
• When to use thematic analysis
• The main approaches to thematic analysis
• The three types of thematic analysis
• How to “do” thematic analysis (the process)
• Tips and suggestions

First, the lingo…

Before we begin, let’s first lay down some terminology. When undertaking thematic analysis, you’ll make use of codes . A code is a label assigned to a piece of text, and the aim of using a code is to identify and summarise important concepts within a set of data, such as an interview transcript.

For example, if you had the sentence, “My rabbit ate my shoes”, you could use the codes “rabbit” or “shoes” to highlight these two concepts. The process of assigning codes is called qualitative coding . If this is a new concept to you, be sure to check out our detailed post about qualitative coding .

Codes are vital as they lay a foundation for themes . But what exactly is a theme? Simply put, a theme is a pattern that can be identified within a data set. In other words, it’s a topic or concept that pops up repeatedly throughout your data. Grouping your codes into themes serves as a way of summarising sections of your data in a useful way that helps you answer your research question(s) and achieve your research aim(s).

Alright – with that out of the way, let’s jump into the wonderful world of thematic analysis…

What is thematic analysis?

Thematic analysis is the study of patterns to uncover meaning . In other words, it’s about analysing the patterns and themes within your data set to identify the underlying meaning. Importantly, this process is driven by your research aims and questions , so it’s not necessary to identify every possible theme in the data, but rather to focus on the key aspects that relate to your research questions .

Although the research questions are a driving force in thematic analysis (and pretty much all analysis methods), it’s important to remember that these questions are not necessarily fixed . As thematic analysis tends to be a bit of an exploratory process, research questions can evolve as you progress with your coding and theme identification.

When should you use thematic analysis?

There are many potential qualitative analysis methods that you can use to analyse a dataset. For example, content analysis , discourse analysis , and narrative analysis are popular choices. So why use thematic analysis?

Thematic analysis is highly beneficial when working with large bodies of data ,  as it allows you to divide and categorise large amounts of data in a way that makes it easier to digest. Thematic analysis is particularly useful when looking for subjective information , such as a participant’s experiences, views, and opinions. For this reason, thematic analysis is often conducted on data derived from interviews , conversations, open-ended survey responses , and social media posts.

Your research questions can also give you an idea of whether you should use thematic analysis or not. For example, if your research questions were to be along the lines of:

• How do dog walkers perceive rules and regulations on dog-friendly beaches?
• What are students’ experiences with the shift to online learning?
• What opinions do health professionals hold about the Hippocratic code?
• How is gender constructed in a high school classroom setting?

These examples are all research questions centering on the subjective experiences of participants and aim to assess experiences, views, and opinions. Therefore, thematic analysis presents a possible approach.

In short, thematic analysis is a good choice when you are wanting to categorise large bodies of data (although the data doesn’t necessarily have to be large), particularly when you are interested in subjective experiences .

What are the main approaches?

Broadly speaking, there are two overarching approaches to thematic analysis: inductive and deductive . The approach you take will depend on what is most suitable in light of your research aims and questions. Let’s have a look at the options.

The inductive approach

The inductive approach involves deriving meaning and creating themes from data without any preconceptions . In other words, you’d dive into your analysis without any idea of what codes and themes will emerge, and thus allow these to emerge from the data.

For example, if you’re investigating typical lunchtime conversational topics in a university faculty, you’d enter the research without any preconceived codes, themes or expected outcomes. Of course, you may have thoughts about what might be discussed (e.g., academic matters because it’s an academic setting), but the objective is to not let these preconceptions inform your analysis.

The inductive approach is best suited to research aims and questions that are exploratory in nature , and cases where there is little existing research on the topic of interest.

The deductive approach

In contrast to the inductive approach, a deductive approach involves jumping into your analysis with a pre-determined set of codes . Usually, this approach is informed by prior knowledge and/or existing theory or empirical research (which you’d cover in your literature review ).

For example, a researcher examining the impact of a specific psychological intervention on mental health outcomes may draw on an existing theoretical framework that includes concepts such as coping strategies, social support, and self-efficacy, using these as a basis for a set of pre-determined codes.

The deductive approach is best suited to research aims and questions that are confirmatory in nature , and cases where there is a lot of existing research on the topic of interest.

Regardless of whether you take the inductive or deductive approach, you’ll also need to decide what level of content your analysis will focus on – specifically, the semantic level or the latent level.

A semantic-level focus ignores the underlying meaning of data , and identifies themes based only on what is explicitly or overtly stated or written – in other words, things are taken at face value.

In contrast, a latent-level focus concentrates on the underlying meanings and looks at the reasons for semantic content. Furthermore, in contrast to the semantic approach, a latent approach involves an element of interpretation , where data is not just taken at face value, but meanings are also theorised.

“But how do I know when to use what approach?”, I hear you ask.

Well, this all depends on the type of data you’re analysing and what you’re trying to achieve with your analysis. For example, if you’re aiming to analyse explicit opinions expressed in interviews and you know what you’re looking for ahead of time (based on a collection of prior studies), you may choose to take a deductive approach with a semantic-level focus.

On the other hand, if you’re looking to explore the underlying meaning expressed by participants in a focus group, and you don’t have any preconceptions about what to expect, you’ll likely opt for an inductive approach with a latent-level focus.

Simply put, the nature and focus of your research, especially your research aims , objectives and questions will  inform the approach you take to thematic analysis.

What are the types of thematic analysis?

Now that you’ve got an understanding of the overarching approaches to thematic analysis, it’s time to have a look at the different types of thematic analysis you can conduct. Broadly speaking, there are three “types” of thematic analysis:

• Reflexive thematic analysis
• Codebook thematic analysis
• Coding reliability thematic analysis

Let’s have a look at each of these:

Reflexive thematic analysis takes an inductive approach, letting the codes and themes emerge from that data. This type of thematic analysis is very flexible, as it allows researchers to change, remove, and add codes as they work through the data. As the name suggests, reflexive thematic analysis emphasizes the active engagement of the researcher in critically reflecting on their assumptions, biases, and interpretations, and how these may shape the analysis.

Reflexive thematic analysis typically involves iterative and reflexive cycles of coding, interpreting, and reflecting on data, with the aim of producing nuanced and contextually sensitive insights into the research topic, while at the same time recognising and addressing the subjective nature of the research process.

Codebook thematic analysis , on the other hand, lays on the opposite end of the spectrum. Taking a deductive approach, this type of thematic analysis makes use of structured codebooks containing clearly defined, predetermined codes. These codes are typically drawn from a combination of existing theoretical theories, empirical studies and prior knowledge of the situation.

Codebook thematic analysis aims to produce reliable and consistent findings. Therefore, it’s often used in studies where a clear and predefined coding framework is desired to ensure rigour and consistency in data analysis.

Coding reliability thematic analysis necessitates the work of multiple coders, and the design is specifically intended for research teams. With this type of analysis, codebooks are typically fixed and are rarely altered.

The benefit of this form of analysis is that it brings an element of intercoder reliability where coders need to agree upon the codes used, which means that the outcome is more rigorous as the element of subjectivity is reduced. In other words, multiple coders discuss which codes should be used and which shouldn’t, and this consensus reduces the bias of having one individual coder decide upon themes.

Quick Recap: Thematic analysis approaches and types

To recap, the two main approaches to thematic analysis are inductive , and deductive . Then we have the three types of thematic analysis: reflexive, codebook and coding reliability . Which type of thematic analysis you opt for will need to be informed by factors such as:

• The approach you are taking. For example, if you opt for an inductive approach, you’ll likely utilise reflexive thematic analysis.
• Whether you’re working alone or in a group . It’s likely that, if you’re doing research as part of your postgraduate studies, you’ll be working alone. This means that you’ll need to choose between reflexive and codebook thematic analysis.

Now that we’ve covered the “what” in terms of thematic analysis approaches and types, it’s time to look at the “how” of thematic analysis.

Need a helping hand?

How to “do” thematic analysis

At this point, you’re ready to get going with your analysis, so let’s dive right into the thematic analysis process. Keep in mind that what we’ll cover here is a generic process, and the relevant steps will vary depending on the approach and type of thematic analysis you opt for.

Step 1: Get familiar with the data

The first step in your thematic analysis involves getting a feel for your data and seeing what general themes pop up. If you’re working with audio data, this is where you’ll do the transcription , converting audio to text.

At this stage, you’ll want to come up with preliminary thoughts about what you’ll code , what codes you’ll use for them, and what codes will accurately describe your content. It’s a good idea to revisit your research topic , and your aims and objectives at this stage. For example, if you’re looking at what people feel about different types of dogs, you can code according to when different breeds are mentioned (e.g., border collie, Labrador, corgi) and when certain feelings/emotions are brought up.

As a general tip, it’s a good idea to keep a reflexivity journal . This is where you’ll write down how you coded your data, why you coded your data in that particular way, and what the outcomes of this data coding are. Using a reflexive journal from the start will benefit you greatly in the final stages of your analysis because you can reflect on the coding process and assess whether you have coded in a manner that is reliable and whether your codes and themes support your findings.

As you can imagine, a reflexivity journal helps to increase reliability as it allows you to analyse your data systematically and consistently. If you choose to make use of a reflexivity journal, this is the stage where you’ll want to take notes about your initial codes and list them in your journal so that you’ll have an idea of what exactly is being reflected in your data. At a later stage in the analysis, this data can be more thoroughly coded, or the identified codes can be divided into more specific ones.

Step 2: Search for patterns or themes in the codes

Step 2! You’re going strong. In this step, you’ll want to look out for patterns or themes in your codes. Moving from codes to themes is not necessarily a smooth or linear process. As you become more and more familiar with the data, you may find that you need to assign different codes or themes according to new elements you find. For example, if you were analysing a text talking about wildlife, you may come across the codes, “pigeon”, “canary” and “budgerigar” which can fall under the theme of birds.

As you work through the data, you may start to identify subthemes , which are subdivisions of themes that focus specifically on an aspect within the theme that is significant or relevant to your research question. For example, if your theme is a university, your subthemes could be faculties or departments at that university.

In this stage of the analysis, your reflexivity journal entries need to reflect how codes were interpreted and combined to form themes.

Step 3: Review themes

By now you’ll have a good idea of your codes, themes, and potentially subthemes. Now it’s time to review all the themes you’ve identified . In this step, you’ll want to check that everything you’ve categorised as a theme actually fits the data, whether the themes do indeed exist in the data, whether there are any themes missing , and whether you can move on to the next step knowing that you’ve coded all your themes accurately and comprehensively . If you find that your themes have become too broad and there is far too much information under one theme, it may be useful to split this into more themes so that you’re able to be more specific with your analysis.

In your reflexivity journal, you’ll want to write about how you understood the themes and how they are supported by evidence, as well as how the themes fit in with your codes. At this point, you’ll also want to revisit your research questions and make sure that the data and themes you’ve identified are directly relevant to these questions .

Step 4: Finalise Themes

By this point, your analysis will really start to take shape. In the previous step, you reviewed and refined your themes, and now it’s time to label and finalise them . It’s important to note here that, just because you’ve moved onto the next step, it doesn’t mean that you can’t go back and revise or rework your themes. In contrast to the previous step, finalising your themes means spelling out what exactly the themes consist of, and describe them in detail . If you struggle with this, you may want to return to your data to make sure that your data and coding do represent the themes, and if you need to divide your themes into more themes (i.e., return to step 3).

When you name your themes, make sure that you select labels that accurately encapsulate the properties of the theme . For example, a theme name such as “enthusiasm in professionals” leaves the question of “who are the professionals?”, so you’d want to be more specific and label the theme as something along the lines of “enthusiasm in healthcare professionals”.

It is very important at this stage that you make sure that your themes align with your research aims and questions . When you’re finalising your themes, you’re also nearing the end of your analysis and need to keep in mind that your final report (discussed in the next step) will need to fit in with the aims and objectives of your research.

In your reflexivity journal, you’ll want to write down a few sentences describing your themes and how you decided on these. Here, you’ll also want to mention how the theme will contribute to the outcomes of your research, and also what it means in relation to your research questions and focus of your research.

By the end of this stage, you’ll be done with your themes – meaning it’s time to write up your findings and produce a report.

Step 5: Produce your report

You’re nearly done! Now that you’ve analysed your data, it’s time to report on your findings. A typical thematic analysis report consists of:

• An introduction
• A methodology section
• Your results and findings
• A conclusion

When writing your report, make sure that you provide enough information for a reader to be able to evaluate the rigour of your analysis. In other words, the reader needs to know the exact process you followed when analysing your data and why. The questions of “what”, “how”, “why”, “who”, and “when” may be useful in this section.

So, what did you investigate? How did you investigate it? Why did you choose this particular method? Who does your research focus on, and who are your participants? When did you conduct your research, when did you collect your data, and when was the data produced? Your reflexivity journal will come in handy here as within it you’ve already labelled, described, and supported your themes.

If you’re undertaking a thematic analysis as part of a dissertation or thesis, this discussion will be split across your methodology, results and discussion chapters . For more information about those chapters, check out our detailed post about dissertation structure .

It’s absolutely vital that, when writing up your results, you back up every single one of your findings with quotations . The reader needs to be able to see that what you’re reporting actually exists within the results. Also make sure that, when reporting your findings, you tie them back to your research questions . You don’t want your reader to be looking through your findings and asking, “So what?”, so make sure that every finding you represent is relevant to your research topic and questions.

Quick Recap: How to “do” thematic analysis

Getting familiar with your data: Here you’ll read through your data and get a general overview of what you’re working with. At this stage, you may identify a few general codes and themes that you’ll make use of in the next step.

Search for patterns or themes in your codes : Here you’ll dive into your data and pick out the themes and codes relevant to your research question(s).

Review themes : In this step, you’ll revisit your codes and themes to make sure that they are all truly representative of the data, and that you can use them in your final report.

Finalise themes : Here’s where you “solidify” your analysis and make it report-ready by describing and defining your themes.

Produce your report : This is the final step of your thematic analysis process, where you put everything you’ve found together and report on your findings.

Tips & Suggestions

In the video below, we share 6 time-saving tips and tricks to help you approach your thematic analysis as effectively and efficiently as possible.

Wrapping Up

In this article, we’ve covered the basics of thematic analysis – what it is, when to use it, the different approaches and types of thematic analysis, and how to perform a thematic analysis.

If you have any questions about thematic analysis, drop a comment below and we’ll do our best to assist. If you’d like 1-on-1 support with your thematic analysis, be sure to check out our research coaching services here .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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21 Comments

I really appreciate the help

Hello Sir, how many levels of coding can be done in thematic analysis? We generate codes from the transcripts, then subthemes from the codes and themes from subthemes, isn’t it? Should these themes be again grouped together? how many themes can be derived?can you please share an example of coding through thematic analysis in a tabular format?

I’ve found the article very educative and useful

Excellent. Very helpful and easy to understand.

This article so far has been most helpful in understanding how to write an analysis chapter. Thank you.

My research topic is the challenges face by the school principal on the process of procurement . Thematic analysis is it sutable fir data analysis ?

It is a great help. Thanks.

Best advice. Worth reading. Thank you.

Where can I find an example of a template analysis table ?

Finally I got the best article . I wish they also have every psychology topics.

Hello, Sir/Maam

I am actually finding difficulty in doing qualitative analysis of my data and how to triangulate this with quantitative data. I encountered your web by accident in the process of searching for a much simplified way of explaining about thematic analysis such as coding, thematic analysis, write up. When your query if I need help popped up, I was hesitant to answer. Because I think this is for fee and I cannot afford. So May I just ask permission to copy for me to read and guide me to study so I can apply it myself for my gathered qualitative data for my graduate study.

Thank you very much! this is very helpful to me in my Graduate research qualitative data analysis.

Thank you very much. I find your guidance here helpful. Kindly let help me understand how to write findings and discussions.

i am having troubles with the concept of framework analysis which i did not find here and i have been an assignment on framework analysis

I was discouraged and felt insecure because after more than a year of writing my thesis, my work seemed lost its direction after being checked. But, I am truly grateful because through the comments, corrections, and guidance of the wisdom of my director, I can already see the bright light because of thematic analysis. I am working with Biblical Texts. And thematic analysis will be my method. Thank you.

lovely and helpful. thanks

very informative information.

thank you very much!, this is very helpful in my report, God bless……..

Thank you for the insight. I am really relieved as you have provided a super guide for my thesis.

Thanks a lot, really enlightening

excellent! very helpful thank a lot for your great efforts

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• Practical thematic...

Practical thematic analysis: a guide for multidisciplinary health services research teams engaging in qualitative analysis

• Related content
• Peer review
• Catherine H Saunders , scientist and assistant professor 1 2 ,
• Ailyn Sierpe , research project coordinator 2 ,
• Christian von Plessen , senior physician 3 ,
• Alice M Kennedy , research project manager 2 4 ,
• Laura C Leviton , senior adviser 5 ,
• Steven L Bernstein , chief research officer 1 ,
• Jenaya Goldwag , resident physician 1 ,
• Joel R King , research assistant 2 ,
• Christine M Marx , patient associate 6 ,
• Jacqueline A Pogue , research project manager 2 ,
• Richard K Saunders , staff physician 1 ,
• Aricca Van Citters , senior research scientist 2 ,
• Renata W Yen , doctoral student 2 ,
• Glyn Elwyn , professor 2 ,
• JoAnna K Leyenaar , associate professor 1 2
• on behalf of the Coproduction Laboratory
• 1 Dartmouth Health, Lebanon, NH, USA
• 2 Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
• 3 Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
• 4 Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping, Sweden
• 5 Highland Park, NJ, USA
• 6 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
• Correspondence to: C H Saunders catherine.hylas.saunders{at}dartmouth.edu
• Accepted 26 April 2023

Qualitative research methods explore and provide deep contextual understanding of real world issues, including people’s beliefs, perspectives, and experiences. Whether through analysis of interviews, focus groups, structured observation, or multimedia data, qualitative methods offer unique insights in applied health services research that other approaches cannot deliver. However, many clinicians and researchers hesitate to use these methods, or might not use them effectively, which can leave relevant areas of inquiry inadequately explored. Thematic analysis is one of the most common and flexible methods to examine qualitative data collected in health services research. This article offers practical thematic analysis as a step-by-step approach to qualitative analysis for health services researchers, with a focus on accessibility for patients, care partners, clinicians, and others new to thematic analysis. Along with detailed instructions covering three steps of reading, coding, and theming, the article includes additional novel and practical guidance on how to draft effective codes, conduct a thematic analysis session, and develop meaningful themes. This approach aims to improve consistency and rigor in thematic analysis, while also making this method more accessible for multidisciplinary research teams.

Through qualitative methods, researchers can provide deep contextual understanding of real world issues, and generate new knowledge to inform hypotheses, theories, research, and clinical care. Approaches to data collection are varied, including interviews, focus groups, structured observation, and analysis of multimedia data, with qualitative research questions aimed at understanding the how and why of human experience. 1 2 Qualitative methods produce unique insights in applied health services research that other approaches cannot deliver. In particular, researchers acknowledge that thematic analysis is a flexible and powerful method of systematically generating robust qualitative research findings by identifying, analysing, and reporting patterns (themes) within data. 3 4 5 6 Although qualitative methods are increasingly valued for answering clinical research questions, many researchers are unsure how to apply them or consider them too time consuming to be useful in responding to practical challenges 7 or pressing situations such as public health emergencies. 8 Consequently, researchers might hesitate to use them, or use them improperly. 9 10 11

Although much has been written about how to perform thematic analysis, practical guidance for non-specialists is sparse. 3 5 6 12 13 In the multidisciplinary field of health services research, qualitative data analysis can confound experienced researchers and novices alike, which can stoke concerns about rigor, particularly for those more familiar with quantitative approaches. 14 Since qualitative methods are an area of specialisation, support from experts is beneficial. However, because non-specialist perspectives can enhance data interpretation and enrich findings, there is a case for making thematic analysis easier, more rapid, and more efficient, 8 particularly for patients, care partners, clinicians, and other stakeholders. A practical guide to thematic analysis might encourage those on the ground to use these methods in their work, unearthing insights that would otherwise remain undiscovered.

Given the need for more accessible qualitative analysis approaches, we present a simple, rigorous, and efficient three step guide for practical thematic analysis. We include new guidance on the mechanics of thematic analysis, including developing codes, constructing meaningful themes, and hosting a thematic analysis session. We also discuss common pitfalls in thematic analysis and how to avoid them.

Summary points

Qualitative methods are increasingly valued in applied health services research, but multidisciplinary research teams often lack accessible step-by-step guidance and might struggle to use these approaches

A newly developed approach, practical thematic analysis, uses three simple steps: reading, coding, and theming

Based on Braun and Clarke’s reflexive thematic analysis, our streamlined yet rigorous approach is designed for multidisciplinary health services research teams, including patients, care partners, and clinicians

This article also provides companion materials including a slide presentation for teaching practical thematic analysis to research teams, a sample thematic analysis session agenda, a theme coproduction template for use during the session, and guidance on using standardised reporting criteria for qualitative research

In their seminal work, Braun and Clarke developed a six phase approach to reflexive thematic analysis. 4 12 We built on their method to develop practical thematic analysis ( box 1 , fig 1 ), which is a simplified and instructive approach that retains the substantive elements of their six phases. Braun and Clarke’s phase 1 (familiarising yourself with the dataset) is represented in our first step of reading. Phase 2 (coding) remains as our second step of coding. Phases 3 (generating initial themes), 4 (developing and reviewing themes), and 5 (refining, defining, and naming themes) are represented in our third step of theming. Phase 6 (writing up) also occurs during this third step of theming, but after a thematic analysis session. 4 12

Key features and applications of practical thematic analysis

Step 1: reading.

All manuscript authors read the data

All manuscript authors write summary memos

Step 2: Coding

Coders perform both data management and early data analysis

Codes are complete thoughts or sentences, not categories

Step 3: Theming

Researchers host a thematic analysis session and share different perspectives

Themes are complete thoughts or sentences, not categories

Applications

For use by practicing clinicians, patients and care partners, students, interdisciplinary teams, and those new to qualitative research

When important insights from healthcare professionals are inaccessible because they do not have qualitative methods training

When time and resources are limited

Steps in practical thematic analysis

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We present linear steps, but as qualitative research is usually iterative, so too is thematic analysis. 15 Qualitative researchers circle back to earlier work to check whether their interpretations still make sense in the light of additional insights, adapting as necessary. While we focus here on the practical application of thematic analysis in health services research, we recognise our approach exists in the context of the broader literature on thematic analysis and the theoretical underpinnings of qualitative methods as a whole. For a more detailed discussion of these theoretical points, as well as other methods widely used in health services research, we recommend reviewing the sources outlined in supplemental material 1. A strong and nuanced understanding of the context and underlying principles of thematic analysis will allow for higher quality research. 16

Practical thematic analysis is a highly flexible approach that can draw out valuable findings and generate new hypotheses, including in cases with a lack of previous research to build on. The approach can also be used with a variety of data, such as transcripts from interviews or focus groups, patient encounter transcripts, professional publications, observational field notes, and online activity logs. Importantly, successful practical thematic analysis is predicated on having high quality data collected with rigorous methods. We do not describe qualitative research design or data collection here. 11 17

In supplemental material 1, we summarise the foundational methods, concepts, and terminology in qualitative research. Along with our guide below, we include a companion slide presentation for teaching practical thematic analysis to research teams in supplemental material 2. We provide a theme coproduction template for teams to use during thematic analysis sessions in supplemental material 3. Our method aligns with the major qualitative reporting frameworks, including the Consolidated Criteria for Reporting Qualitative Research (COREQ). 18 We indicate the corresponding step in practical thematic analysis for each COREQ item in supplemental material 4.

Familiarisation and memoing

We encourage all manuscript authors to review the full dataset (eg, interview transcripts) to familiarise themselves with it. This task is most critical for those who will later be engaged in the coding and theming steps. Although time consuming, it is the best way to involve team members in the intellectual work of data interpretation, so that they can contribute to the analysis and contextualise the results. If this task is not feasible given time limitations or large quantities of data, the data can be divided across team members. In this case, each piece of data should be read by at least two individuals who ideally represent different professional roles or perspectives.

We recommend that researchers reflect on the data and independently write memos, defined as brief notes on thoughts and questions that arise during reading, and a summary of their impressions of the dataset. 2 19 Memoing is an opportunity to gain insights from varying perspectives, particularly from patients, care partners, clinicians, and others. It also gives researchers the opportunity to begin to scope which elements of and concepts in the dataset are relevant to the research question.

Data saturation

The concept of data saturation ( box 2 ) is a foundation of qualitative research. It is defined as the point in analysis at which new data tend to be redundant of data already collected. 21 Qualitative researchers are expected to report their approach to data saturation. 18 Because thematic analysis is iterative, the team should discuss saturation throughout the entire process, beginning with data collection and continuing through all steps of the analysis. 22 During step 1 (reading), team members might discuss data saturation in the context of summary memos. Conversations about saturation continue during step 2 (coding), with confirmation that saturation has been achieved during step 3 (theming). As a rule of thumb, researchers can often achieve saturation in 9-17 interviews or 4-8 focus groups, but this will vary depending on the specific characteristics of the study. 23

Data saturation in context

Braun and Clarke discourage the use of data saturation to determine sample size (eg, number of interviews), because it assumes that there is an objective truth to be captured in the data (sometimes known as a positivist perspective). 20 Qualitative researchers often try to avoid positivist approaches, arguing that there is no one true way of seeing the world, and will instead aim to gather multiple perspectives. 5 Although this theoretical debate with qualitative methods is important, we recognise that a priori estimates of saturation are often needed, particularly for investigators newer to qualitative research who might want a more pragmatic and applied approach. In addition, saturation based, sample size estimation can be particularly helpful in grant proposals. However, researchers should still follow a priori sample size estimation with a discussion to confirm saturation has been achieved.

Definition of coding

We describe codes as labels for concepts in the data that are directly relevant to the study objective. Historically, the purpose of coding was to distil the large amount of data collected into conceptually similar buckets so that researchers could review it in aggregate and identify key themes. 5 24 We advocate for a more analytical approach than is typical with thematic analysis. With our method, coding is both the foundation for and the beginning of thematic analysis—that is, early data analysis, management, and reduction occur simultaneously rather than as different steps. This approach moves the team more efficiently towards being able to describe themes.

Building the coding team

Coders are the research team members who directly assign codes to the data, reading all material and systematically labelling relevant data with appropriate codes. Ideally, at least two researchers would code every discrete data document, such as one interview transcript. 25 If this task is not possible, individual coders can each code a subset of the data that is carefully selected for key characteristics (sometimes known as purposive selection). 26 When using this approach, we recommend that at least 10% of data be coded by two or more coders to ensure consistency in codebook application. We also recommend coding teams of no more than four to five people, for practical reasons concerning maintaining consistency.

Clinicians, patients, and care partners bring unique perspectives to coding and enrich the analytical process. 27 Therefore, we recommend choosing coders with a mix of relevant experiences so that they can challenge and contextualise each other’s interpretations based on their own perspectives and opinions ( box 3 ). We recommend including both coders who collected the data and those who are naive to it, if possible, given their different perspectives. We also recommend all coders review the summary memos from the reading step so that key concepts identified by those not involved in coding can be integrated into the analytical process. In practice, this review means coding the memos themselves and discussing them during the code development process. This approach ensures that the team considers a diversity of perspectives.

Coding teams in context

The recommendation to use multiple coders is a departure from Braun and Clarke. 28 29 When the views, experiences, and training of each coder (sometimes known as positionality) 30 are carefully considered, having multiple coders can enhance interpretation and enrich findings. When these perspectives are combined in a team setting, researchers can create shared meaning from the data. Along with the practical consideration of distributing the workload, 31 inclusion of these multiple perspectives increases the overall quality of the analysis by mitigating the impact of any one coder’s perspective. 30

Coding tools

Qualitative analysis software facilitates coding and managing large datasets but does not perform the analytical work. The researchers must perform the analysis themselves. Most programs support queries and collaborative coding by multiple users. 32 Important factors to consider when choosing software can include accessibility, cost, interoperability, the look and feel of code reports, and the ease of colour coding and merging codes. Coders can also use low tech solutions, including highlighters, word processors, or spreadsheets.

Drafting effective codes

To draft effective codes, we recommend that the coders review each document line by line. 33 As they progress, they can assign codes to segments of data representing passages of interest. 34 Coders can also assign multiple codes to the same passage. Consensus among coders on what constitutes a minimum or maximum amount of text for assigning a code is helpful. As a general rule, meaningful segments of text for coding are shorter than one paragraph, but longer than a few words. Coders should keep the study objective in mind when determining which data are relevant ( box 4 ).

Code types in context

Similar to Braun and Clarke’s approach, practical thematic analysis does not specify whether codes are based on what is evident from the data (sometimes known as semantic) or whether they are based on what can be inferred at a deeper level from the data (sometimes known as latent). 4 12 35 It also does not specify whether they are derived from the data (sometimes known as inductive) or determined ahead of time (sometimes known as deductive). 11 35 Instead, it should be noted that health services researchers conducting qualitative studies often adopt all these approaches to coding (sometimes known as hybrid analysis). 3

In practical thematic analysis, codes should be more descriptive than general categorical labels that simply group data with shared characteristics. At a minimum, codes should form a complete (or full) thought. An easy way to conceptualise full thought codes is as complete sentences with subjects and verbs ( table 1 ), although full sentence coding is not always necessary. With full thought codes, researchers think about the data more deeply and capture this insight in the codes. This coding facilitates the entire analytical process and is especially valuable when moving from codes to broader themes. Experienced qualitative researchers often intuitively use full thought or sentence codes, but this practice has not been explicitly articulated as a path to higher quality coding elsewhere in the literature. 6

Example transcript with codes used in practical thematic analysis 36

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Depending on the nature of the data, codes might either fall into flat categories or be arranged hierarchically. Flat categories are most common when the data deal with topics on the same conceptual level. In other words, one topic is not a subset of another topic. By contrast, hierarchical codes are more appropriate for concepts that naturally fall above or below each other. Hierarchical coding can also be a useful form of data management and might be necessary when working with a large or complex dataset. 5 Codes grouped into these categories can also make it easier to naturally transition into generating themes from the initial codes. 5 These decisions between flat versus hierarchical coding are part of the work of the coding team. In both cases, coders should ensure that their code structures are guided by their research questions.

Developing the codebook

A codebook is a shared document that lists code labels and comprehensive descriptions for each code, as well as examples observed within the data. Good code descriptions are precise and specific so that coders can consistently assign the same codes to relevant data or articulate why another coder would do so. Codebook development is iterative and involves input from the entire coding team. However, as those closest to the data, coders must resist undue influence, real or perceived, from other team members with conflicting opinions—it is important to mitigate the risk that more senior researchers, like principal investigators, exert undue influence on the coders’ perspectives.

In practical thematic analysis, coders begin codebook development by independently coding a small portion of the data, such as two to three transcripts or other units of analysis. Coders then individually produce their initial codebooks. This task will require them to reflect on, organise, and clarify codes. The coders then meet to reconcile the draft codebooks, which can often be difficult, as some coders tend to lump several concepts together while others will split them into more specific codes. Discussing disagreements and negotiating consensus are necessary parts of early data analysis. Once the codebook is relatively stable, we recommend soliciting input on the codes from all manuscript authors. Yet, coders must ultimately be empowered to finalise the details so that they are comfortable working with the codebook across a large quantity of data.

Assigning codes to the data

After developing the codebook, coders will use it to assign codes to the remaining data. While the codebook’s overall structure should remain constant, coders might continue to add codes corresponding to any new concepts observed in the data. If new codes are added, coders should review the data they have already coded and determine whether the new codes apply. Qualitative data analysis software can be useful for editing or merging codes.

We recommend that coders periodically compare their code occurrences ( box 5 ), with more frequent check-ins if substantial disagreements occur. In the event of large discrepancies in the codes assigned, coders should revise the codebook to ensure that code descriptions are sufficiently clear and comprehensive to support coding alignment going forward. Because coding is an iterative process, the team can adjust the codebook as needed. 5 28 29

Quantitative coding in context

Researchers should generally avoid reporting code counts in thematic analysis. However, counts can be a useful proxy in maintaining alignment between coders on key concepts. 26 In practice, therefore, researchers should make sure that all coders working on the same piece of data assign the same codes with a similar pattern and that their memoing and overall assessment of the data are aligned. 37 However, the frequency of a code alone is not an indicator of its importance. It is more important that coders agree on the most salient points in the data; reviewing and discussing summary memos can be helpful here. 5

Researchers might disagree on whether or not to calculate and report inter-rater reliability. We note that quantitative tests for agreement, such as kappa statistics or intraclass correlation coefficients, can be distracting and might not provide meaningful results in qualitative analyses. Similarly, Braun and Clarke argue that expecting perfect alignment on coding is inconsistent with the goal of co-constructing meaning. 28 29 Overall consensus on codes’ salience and contributions to themes is the most important factor.

Definition of themes

Themes are meta-constructs that rise above codes and unite the dataset ( box 6 , fig 2 ). They should be clearly evident, repeated throughout the dataset, and relevant to the research questions. 38 While codes are often explicit descriptions of the content in the dataset, themes are usually more conceptual and knit the codes together. 39 Some researchers hypothesise that theme development is loosely described in the literature because qualitative researchers simply intuit themes during the analytical process. 39 In practical thematic analysis, we offer a concrete process that should make developing meaningful themes straightforward.

Themes in context

According to Braun and Clarke, a theme “captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set.” 4 Similarly, Braun and Clarke advise against themes as domain summaries. While different approaches can draw out themes from codes, the process begins by identifying patterns. 28 35 Like Braun and Clarke and others, we recommend that researchers consider the salience of certain themes, their prevalence in the dataset, and their keyness (ie, how relevant the themes are to the overarching research questions). 4 12 34

Use of themes in practical thematic analysis

Constructing meaningful themes

After coding all the data, each coder should independently reflect on the team’s summary memos (step 1), the codebook (step 2), and the coded data itself to develop draft themes (step 3). It can be illuminating for coders to review all excerpts associated with each code, so that they derive themes directly from the data. Researchers should remain focused on the research question during this step, so that themes have a clear relation with the overall project aim. Use of qualitative analysis software will make it easy to view each segment of data tagged with each code. Themes might neatly correspond to groups of codes. Or—more likely—they will unite codes and data in unexpected ways. A whiteboard or presentation slides might be helpful to organise, craft, and revise themes. We also provide a template for coproducing themes (supplemental material 3). As with codebook justification, team members will ideally produce individual drafts of the themes that they have identified in the data. They can then discuss these with the group and reach alignment or consensus on the final themes.

The team should ensure that all themes are salient, meaning that they are: supported by the data, relevant to the study objectives, and important. Similar to codes, themes are framed as complete thoughts or sentences, not categories. While codes and themes might appear to be similar to each other, the key distinction is that the themes represent a broader concept. Table 2 shows examples of codes and their corresponding themes from a previously published project that used practical thematic analysis. 36 Identifying three to four key themes that comprise a broader overarching theme is a useful approach. Themes can also have subthemes, if appropriate. 40 41 42 43 44

Example codes with themes in practical thematic analysis 36

Thematic analysis session

After each coder has independently produced draft themes, a carefully selected subset of the manuscript team meets for a thematic analysis session ( table 3 ). The purpose of this session is to discuss and reach alignment or consensus on the final themes. We recommend a session of three to five hours, either in-person or virtually.

Example agenda of thematic analysis session

The composition of the thematic analysis session team is important, as each person’s perspectives will shape the results. This group is usually a small subset of the broader research team, with three to seven individuals. We recommend that primary and senior authors work together to include people with diverse experiences related to the research topic. They should aim for a range of personalities and professional identities, particularly those of clinicians, trainees, patients, and care partners. At a minimum, all coders and primary and senior authors should participate in the thematic analysis session.

The session begins with each coder presenting their draft themes with supporting quotes from the data. 5 Through respectful and collaborative deliberation, the group will develop a shared set of final themes.

One team member facilitates the session. A firm, confident, and consistent facilitation style with good listening skills is critical. For practical reasons, this person is not usually one of the primary coders. Hierarchies in teams cannot be entirely flattened, but acknowledging them and appointing an external facilitator can reduce their impact. The facilitator can ensure that all voices are heard. For example, they might ask for perspectives from patient partners or more junior researchers, and follow up on comments from senior researchers to say, “We have heard your perspective and it is important; we want to make sure all perspectives in the room are equally considered.” Or, “I hear [senior person] is offering [x] idea, I’d like to hear other perspectives in the room.” The role of the facilitator is critical in the thematic analysis session. The facilitator might also privately discuss with more senior researchers, such as principal investigators and senior authors, the importance of being aware of their influence over others and respecting and eliciting the perspectives of more junior researchers, such as patients, care partners, and students.

To our knowledge, this discrete thematic analysis session is a novel contribution of practical thematic analysis. It helps efficiently incorporate diverse perspectives using the session agenda and theme coproduction template (supplemental material 3) and makes the process of constructing themes transparent to the entire research team.

Writing the report

We recommend beginning the results narrative with a summary of all relevant themes emerging from the analysis, followed by a subheading for each theme. Each subsection begins with a brief description of the theme and is illustrated with relevant quotes, which are contextualised and explained. The write-up should not simply be a list, but should contain meaningful analysis and insight from the researchers, including descriptions of how different stakeholders might have experienced a particular situation differently or unexpectedly.

In addition to weaving quotes into the results narrative, quotes can be presented in a table. This strategy is a particularly helpful when submitting to clinical journals with tight word count limitations. Quote tables might also be effective in illustrating areas of agreement and disagreement across stakeholder groups, with columns representing different groups and rows representing each theme or subtheme. Quotes should include an anonymous label for each participant and any relevant characteristics, such as role or gender. The aim is to produce rich descriptions. 5 We recommend against repeating quotations across multiple themes in the report, so as to avoid confusion. The template for coproducing themes (supplemental material 3) allows documentation of quotes supporting each theme, which might also be useful during report writing.

Visual illustrations such as a thematic map or figure of the findings can help communicate themes efficiently. 4 36 42 44 If a figure is not possible, a simple list can suffice. 36 Both must clearly present the main themes with subthemes. Thematic figures can facilitate confirmation that the researchers’ interpretations reflect the study populations’ perspectives (sometimes known as member checking), because authors can invite discussions about the figure and descriptions of findings and supporting quotes. 46 This process can enhance the validity of the results. 46

In supplemental material 4, we provide additional guidance on reporting thematic analysis consistent with COREQ. 18 Commonly used in health services research, COREQ outlines a standardised list of items to be included in qualitative research reports ( box 7 ).

Reporting in context

We note that use of COREQ or any other reporting guidelines does not in itself produce high quality work and should not be used as a substitute for general methodological rigor. Rather, researchers must consider rigor throughout the entire research process. As the issue of how to conceptualise and achieve rigorous qualitative research continues to be debated, 47 48 we encourage researchers to explicitly discuss how they have looked at methodological rigor in their reports. Specifically, we point researchers to Braun and Clarke’s 2021 tool for evaluating thematic analysis manuscripts for publication (“Twenty questions to guide assessment of TA [thematic analysis] research quality”). 16

Avoiding common pitfalls

Awareness of common mistakes can help researchers avoid improper use of qualitative methods. Improper use can, for example, prevent researchers from developing meaningful themes and can risk drawing inappropriate conclusions from the data. Braun and Clarke also warn of poor quality in qualitative research, noting that “coherence and integrity of published research does not always hold.” 16

Weak themes

An important distinction between high and low quality themes is that high quality themes are descriptive and complete thoughts. As such, they often contain subjects and verbs, and can be expressed as full sentences ( table 2 ). Themes that are simply descriptive categories or topics could fail to impart meaningful knowledge beyond categorisation. 16 49 50

Researchers will often move from coding directly to writing up themes, without performing the work of theming or hosting a thematic analysis session. Skipping concerted theming often results in themes that look more like categories than unifying threads across the data.

Unfocused analysis

Because data collection for qualitative research is often semi-structured (eg, interviews, focus groups), not all data will be directly relevant to the research question at hand. To avoid unfocused analysis and a correspondingly unfocused manuscript, we recommend that all team members keep the research objective in front of them at every stage, from reading to coding to theming. During the thematic analysis session, we recommend that the research question be written on a whiteboard so that all team members can refer back to it, and so that the facilitator can ensure that conversations about themes occur in the context of this question. Consistently focusing on the research question can help to ensure that the final report directly answers it, as opposed to the many other interesting insights that might emerge during the qualitative research process. Such insights can be picked up in a secondary analysis if desired.

Inappropriate quantification

Presenting findings quantitatively (eg, “We found 18 instances of participants mentioning safety concerns about the vaccines”) is generally undesirable in practical thematic analysis reporting. 51 Descriptive terms are more appropriate (eg, “participants had substantial concerns about the vaccines,” or “several participants were concerned about this”). This descriptive presentation is critical because qualitative data might not be consistently elicited across participants, meaning that some individuals might share certain information while others do not, simply based on how conversations evolve. Additionally, qualitative research does not aim to draw inferences outside its specific sample. Emphasising numbers in thematic analysis can lead to readers incorrectly generalising the findings. Although peer reviewers unfamiliar with thematic analysis often request this type of quantification, practitioners of practical thematic analysis can confidently defend their decision to avoid it. If quantification is methodologically important, we recommend simultaneously conducting a survey or incorporating standardised interview techniques into the interview guide. 11

Neglecting group dynamics

Researchers should concertedly consider group dynamics in the research team. Particular attention should be paid to power relations and the personality of team members, which can include aspects such as who most often speaks, who defines concepts, and who resolves disagreements that might arise within the group. 52

The perspectives of patient and care partners are particularly important to cultivate. Ideally, patient partners are meaningfully embedded in studies from start to finish, not just for practical thematic analysis. 53 Meaningful engagement can build trust, which makes it easier for patient partners to ask questions, request clarification, and share their perspectives. Professional team members should actively encourage patient partners by emphasising that their expertise is critically important and valued. Noting when a patient partner might be best positioned to offer their perspective can be particularly powerful.

Insufficient time allocation

Researchers must allocate enough time to complete thematic analysis. Working with qualitative data takes time, especially because it is often not a linear process. As the strength of thematic analysis lies in its ability to make use of the rich details and complexities of the data, we recommend careful planning for the time required to read and code each document.

Estimating the necessary time can be challenging. For step 1 (reading), researchers can roughly calculate the time required based on the time needed to read and reflect on one piece of data. For step 2 (coding), the total amount of time needed can be extrapolated from the time needed to code one document during codebook development. We also recommend three to five hours for the thematic analysis session itself, although coders will need to independently develop their draft themes beforehand. Although the time required for practical thematic analysis is variable, teams should be able to estimate their own required effort with these guidelines.

Practical thematic analysis builds on the foundational work of Braun and Clarke. 4 16 We have reframed their six phase process into three condensed steps of reading, coding, and theming. While we have maintained important elements of Braun and Clarke’s reflexive thematic analysis, we believe that practical thematic analysis is conceptually simpler and easier to teach to less experienced researchers and non-researcher stakeholders. For teams with different levels of familiarity with qualitative methods, this approach presents a clear roadmap to the reading, coding, and theming of qualitative data. Our practical thematic analysis approach promotes efficient learning by doing—experiential learning. 12 29 Practical thematic analysis avoids the risk of relying on complex descriptions of methods and theory and places more emphasis on obtaining meaningful insights from those close to real world clinical environments. Although practical thematic analysis can be used to perform intensive theory based analyses, it lends itself more readily to accelerated, pragmatic approaches.

Strengths and limitations

Our approach is designed to smooth the qualitative analysis process and yield high quality themes. Yet, researchers should note that poorly performed analyses will still produce low quality results. Practical thematic analysis is a qualitative analytical approach; it does not look at study design, data collection, or other important elements of qualitative research. It also might not be the right choice for every qualitative research project. We recommend it for applied health services research questions, where diverse perspectives and simplicity might be valuable.

We also urge researchers to improve internal validity through triangulation methods, such as member checking (supplemental material 1). 46 Member checking could include soliciting input on high level themes, theme definitions, and quotations from participants. This approach might increase rigor.

Implications

We hope that by providing clear and simple instructions for practical thematic analysis, a broader range of researchers will be more inclined to use these methods. Increased transparency and familiarity with qualitative approaches can enhance researchers’ ability to both interpret qualitative studies and offer up new findings themselves. In addition, it can have usefulness in training and reporting. A major strength of this approach is to facilitate meaningful inclusion of patient and care partner perspectives, because their lived experiences can be particularly valuable in data interpretation and the resulting findings. 11 30 As clinicians are especially pressed for time, they might also appreciate a practical set of instructions that can be immediately used to leverage their insights and access to patients and clinical settings, and increase the impact of qualitative research through timely results. 8

Practical thematic analysis is a simplified approach to performing thematic analysis in health services research, a field where the experiences of patients, care partners, and clinicians are of inherent interest. We hope that it will be accessible to those individuals new to qualitative methods, including patients, care partners, clinicians, and other health services researchers. We intend to empower multidisciplinary research teams to explore unanswered questions and make new, important, and rigorous contributions to our understanding of important clinical and health systems research.

Acknowledgments

All members of the Coproduction Laboratory provided input that shaped this manuscript during laboratory meetings. We acknowledge advice from Elizabeth Carpenter-Song, an expert in qualitative methods.

Coproduction Laboratory group contributors: Stephanie C Acquilano ( http://orcid.org/0000-0002-1215-5531 ), Julie Doherty ( http://orcid.org/0000-0002-5279-6536 ), Rachel C Forcino ( http://orcid.org/0000-0001-9938-4830 ), Tina Foster ( http://orcid.org/0000-0001-6239-4031 ), Megan Holthoff, Christopher R Jacobs ( http://orcid.org/0000-0001-5324-8657 ), Lisa C Johnson ( http://orcid.org/0000-0001-7448-4931 ), Elaine T Kiriakopoulos, Kathryn Kirkland ( http://orcid.org/0000-0002-9851-926X ), Meredith A MacMartin ( http://orcid.org/0000-0002-6614-6091 ), Emily A Morgan, Eugene Nelson, Elizabeth O’Donnell, Brant Oliver ( http://orcid.org/0000-0002-7399-622X ), Danielle Schubbe ( http://orcid.org/0000-0002-9858-1805 ), Gabrielle Stevens ( http://orcid.org/0000-0001-9001-178X ), Rachael P Thomeer ( http://orcid.org/0000-0002-5974-3840 ).

Contributors: Practical thematic analysis, an approach designed for multidisciplinary health services teams new to qualitative research, was based on CHS’s experiences teaching thematic analysis to clinical teams and students. We have drawn heavily from qualitative methods literature. CHS is the guarantor of the article. CHS, AS, CvP, AMK, JRK, and JAP contributed to drafting the manuscript. AS, JG, CMM, JAP, and RWY provided feedback on their experiences using practical thematic analysis. CvP, LCL, SLB, AVC, GE, and JKL advised on qualitative methods in health services research, given extensive experience. All authors meaningfully edited the manuscript content, including AVC and RKS. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This manuscript did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Provenance and peer review: Not commissioned; externally peer reviewed.

• Ziebland S ,
• ↵ A Hybrid Approach to Thematic Analysis in Qualitative Research: Using a Practical Example. 2018. https://methods.sagepub.com/case/hybrid-approach-thematic-analysis-qualitative-research-a-practical-example .
• Maguire M ,
• Vindrola-Padros C ,
• Vindrola-Padros B
• ↵ Vindrola-Padros C. Rapid Ethnographies: A Practical Guide . Cambridge University Press 2021. https://play.google.com/store/books/details?id=n80HEAAAQBAJ
• Schroter S ,
• Merino JG ,
• Barbeau A ,
• ↵ Padgett DK. Qualitative and Mixed Methods in Public Health . SAGE Publications 2011. https://play.google.com/store/books/details?id=LcYgAQAAQBAJ
• Scharp KM ,
• Korstjens I
• Barnett-Page E ,
• ↵ Guest G, Namey EE, Mitchell ML. Collecting Qualitative Data: A Field Manual for Applied Research . SAGE 2013. https://play.google.com/store/books/details?id=-3rmWYKtloC
• Sainsbury P ,
• Emerson RM ,
• Saunders B ,
• Kingstone T ,
• Hennink MM ,
• Kaiser BN ,
• Hennink M ,
• O’Connor C ,
• ↵ Yen RW, Schubbe D, Walling L, et al. Patient engagement in the What Matters Most trial: experiences and future implications for research. Poster presented at International Shared Decision Making conference, Quebec City, Canada. July 2019.
• ↵ Got questions about Thematic Analysis? We have prepared some answers to common ones. https://www.thematicanalysis.net/faqs/ (accessed 9 Nov 2022).
• ↵ Braun V, Clarke V. Thematic Analysis. SAGE Publications. 2022. https://uk.sagepub.com/en-gb/eur/thematic-analysis/book248481 .
• Kalpokas N ,
• Radivojevic I
• Campbell KA ,
• Durepos P ,
• ↵ Understanding Thematic Analysis. https://www.thematicanalysis.net/understanding-ta/ .
• Saunders CH ,
• Stevens G ,
• CONFIDENT Study Long-Term Care Partners
• MacQueen K ,
• Vaismoradi M ,
• Turunen H ,
• Schott SL ,
• Berkowitz J ,
• Carpenter-Song EA ,
• Goldwag JL ,
• Durand MA ,
• Goldwag J ,
• Saunders C ,
• Mishra MK ,
• Rodriguez HP ,
• Shortell SM ,
• Verdinelli S ,
• Scagnoli NI
• Campbell C ,
• Sparkes AC ,
• McGannon KR
• Sandelowski M ,
• Connelly LM ,
• O’Malley AJ ,

• How it works

Thematic Analysis – A Guide with Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

Thematic analysis is one of the most important types of analysis used for qualitative data . When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the speaker.

Moreover, with the help of this analysis, data can be simplified.  

Importance of Thematic Analysis

Thematic analysis has so many unique and dynamic features, some of which are given below:

Thematic analysis is used because:

• It is flexible.
• It is best for complex data sets.
• It is applied to qualitative data sets.
• It takes less complexity compared to other theories of analysis.

Intellectuals and researchers give preference to thematic analysis due to its effectiveness in the research.

How to Conduct a Thematic Analysis?

While doing any research , if your data and procedure are clear, it will be easier for your reader to understand how you concluded the results . This will add much clarity to your research.

Understand the Data

This is the first step of your thematic analysis. At this stage, you have to understand the data set. You need to read the entire data instead of reading the small portion. If you do not have the data in the textual form, you have to transcribe it.

Example: If you are visiting an adult dating website, you have to make a data corpus. You should read and re-read the data and consider several profiles. It will give you an idea of how adults represent themselves on dating sites. You may get the following results:

I am a tall, single(widowed), easy-going, honest, good listener with a good sense of humor. Being a handyperson, I keep busy working around the house, and I also like to follow my favourite hockey team on TV or spoil my two granddaughters when I get the chance!! Enjoy most music except Rap! I keep fit by jogging, walking, and bicycling (at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times, and adventures together

I enjoy photography, lapidary & seeking collectibles in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception.

Development of Initial Coding:

At this stage, you have to do coding. It’s the essential step of your research . Here you have two options for coding. Either you can do the coding manually or take the help of any tool. A software named the NOVIC is considered the best tool for doing automatic coding.

For manual coding, you can follow the steps given below:

• Please write down the data in a proper format so that it can be easier to proceed.
• Use a highlighter to highlight all the essential points from data.
• Make as many points as possible.
• Take notes very carefully at this stage.
• Apply themes as much possible.
• Now check out the themes of the same pattern or concept.
• Turn all the same themes into the single one.

Example: For better understanding, the previously explained example of Step 1 is continued here. You can observe the coded profiles below:

Make Themes

At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.

Extracted Data Review

Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.

For better understanding, a mind-mapping example is given here:

Reviewing all the Themes Again

You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation. 

You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.

Corpus Data

Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.

When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:

Define all the Themes here

Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.

The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.

Does your Research Methodology Have the Following?

• Great Research/Sources
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• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.

Make a Report

You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.

While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.  

Frequently Asked Questions

What is meant by thematic analysis.

Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.

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The authenticity of dissertation is largely influenced by the research method employed. Here we present the most notable research methods for dissertation.

A survey includes questions relevant to the research topic. The participants are selected, and the questionnaire is distributed to collect the data.

Quantitative research is associated with measurable numerical data. Qualitative research is where a researcher collects evidence to seek answers to a question.

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How to do a thematic analysis

What is a thematic analysis?

When is thematic analysis used, braun and clarke’s reflexive thematic analysis, the six steps of thematic analysis, 1. familiarizing, 2. generating initial codes, 3. generating themes, 4. reviewing themes, 5. defining and naming themes, 6. creating the report, the advantages and disadvantages of thematic analysis, disadvantages, frequently asked questions about thematic analysis, related articles.

Thematic analysis is a broad term that describes an approach to analyzing qualitative data . This approach can encompass diverse methods and is usually applied to a collection of texts, such as survey responses and transcriptions of interviews or focus group discussions. Learn more about different research methods.

A researcher performing a thematic analysis will study a set of data to pinpoint repeating patterns, or themes, in the topics and ideas that are expressed in the texts.

In analyzing qualitative data, thematic analysis focuses on concepts, opinions, and experiences, as opposed to pure statistics. This requires an approach to data that is complex and exploratory and can be anchored by different philosophical and conceptual foundations.

A six-step system was developed to help establish clarity and rigor around this process, and it is this system that is most commonly used when conducting a thematic analysis. The six steps are:

• Familiarization
• Generating codes
• Generating themes
• Reviewing themes
• Defining and naming themes
• Creating the report

It is important to note that even though the six steps are listed in sequence, thematic analysis is not necessarily a linear process that advances forward in a one-way, predictable fashion from step one through step six. Rather, it involves a more fluid shifting back and forth between the phases, adjusting to accommodate new insights when they arise.

And arriving at insight is a key goal of this approach. A good thematic analysis doesn’t just seek to present or summarize data. It interprets and makes a statement about it; it extracts meaning from the data.

Since thematic analysis is used to study qualitative data, it works best in cases where you’re looking to gather information about people’s views, values, opinions, experiences, and knowledge.

Some examples of research questions that thematic analysis can be used to answer are:

• What are senior citizens’ experiences of long-term care homes?
• How do women view social media sites as a tool for professional networking?
• How do non-religious people perceive the role of the church in a society?
• What are financial analysts’ ideas and opinions about cryptocurrency?

To begin answering these questions, you would need to gather data from participants who can provide relevant responses. Once you have the data, you would then analyze and interpret it.

Because you’re dealing with personal views and opinions, there is a lot of room for flexibility in terms of how you interpret the data. In this way, thematic analysis is systematic but not purely scientific.

A landmark 2006 paper by Victoria Braun and Victoria Clarke (“ Using thematic analysis in psychology ”) established parameters around thematic analysis—what it is and how to go about it in a systematic way—which had until then been widely used but poorly defined.

Since then, their work has been updated, with the name being revised, notably, to “reflexive thematic analysis.”

One common misconception that Braun and Clarke have taken pains to clarify about their work is that they do not believe that themes “emerge” from the data. To think otherwise is problematic since this suggests that meaning is somehow inherent to the data and that a researcher is merely an objective medium who identifies that meaning.

Conversely, Braun and Clarke view analysis as an interactive process in which the researcher is an active participant in constructing meaning, rather than simply identifying it.

The six stages they presented in their paper are still the benchmark for conducting a thematic analysis. They are presented below.

This step is where you take a broad, high-level view of your data, looking at it as a whole and taking note of your first impressions.

This typically involves reading through written survey responses and other texts, transcribing audio, and recording any patterns that you notice. It’s important to read through and revisit the data in its entirety several times during this stage so that you develop a thorough grasp of all your data.

After familiarizing yourself with your data, the next step is coding notable features of the data in a methodical way. This often means highlighting portions of the text and applying labels, aka codes, to them that describe the nature of their content.

In our example scenario, we’re researching the experiences of women over the age of 50 on professional networking social media sites. Interviews were conducted to gather data, with the following excerpt from one interview.

In the example interview snippet, portions have been highlighted and coded. The codes describe the idea or perception described in the text.

It pays to be exhaustive and thorough at this stage. Good practice involves scrutinizing the data several times, since new information and insight may become apparent upon further review that didn’t jump out at first glance. Multiple rounds of analysis also allow for the generation of more new codes.

Once the text is thoroughly reviewed, it’s time to collate the data into groups according to their code.

Now that we’ve created our codes, we can examine them, identify patterns within them, and begin generating themes.

Keep in mind that themes are more encompassing than codes. In general, you’ll be bundling multiple codes into a single theme.

To draw on the example we used above about women and networking through social media, codes could be combined into themes in the following way:

You’ll also be curating your codes and may elect to discard some on the basis that they are too broad or not directly relevant. You may also choose to redefine some of your codes as themes and integrate other codes into them. It all depends on the purpose and goal of your research.

This is the stage where we check that the themes we’ve generated accurately and relevantly represent the data they are based on. Once again, it’s beneficial to take a thorough, back-and-forth approach that includes review, assessment, comparison, and inquiry. The following questions can support the review:

• Has anything been overlooked?
• Are the themes definitively supported by the data?
• Is there any room for improvement?

With your final list of themes in hand, the next step is to name and define them.

In defining them, we want to nail down the meaning of each theme and, importantly, how it allows us to make sense of the data.

Once you have your themes defined, you’ll need to apply a concise and straightforward name to each one.

In our example, our “perceived lack of skills” may be adjusted to reflect that the texts expressed uncertainty about skills rather than the definitive absence of them. In this case, a more apt name for the theme might be “questions about competence.”

To finish the process, we put our findings down in writing. As with all scholarly writing, a thematic analysis should open with an introduction section that explains the research question and approach.

This is followed by a statement about the methodology that includes how data was collected and how the thematic analysis was performed.

Each theme is addressed in detail in the results section, with attention paid to the frequency and presence of the themes in the data, as well as what they mean, and with examples from the data included as supporting evidence.

The conclusion section describes how the analysis answers the research question and summarizes the key points.

In our example, the conclusion may assert that it is common for women over the age of 50 to have negative experiences on professional networking sites, and that these are often tied to interactions with other users and a sense that using these sites requires specialized skills.

Thematic analysis is useful for analyzing large data sets, and it allows a lot of flexibility in terms of designing theoretical and research frameworks. Moreover, it supports the generation and interpretation of themes that are backed by data.

There are times when thematic analysis is not the best approach to take because it can be highly subjective, and, in seeking to identify broad patterns, it can overlook nuance in the data.

What’s more, researchers must be judicious about reflecting on how their own position and perspective bears on their interpretations of the data and if they are imposing meaning that is not there or failing to pick up on meaning that is.

Thematic analysis offers a flexible and recursive way to approach qualitative data that has the potential to yield valuable insights about people’s opinions, views, and lived experience. It must be applied, however, in a conscientious fashion so as not to allow subjectivity to taint or obscure the results.

The purpose of thematic analysis is to find repeating patterns, or themes, in qualitative data. Thematic analysis can encompass diverse methods and is usually applied to a collection of texts, such as survey responses and transcriptions of interviews or focus group discussions. In analyzing qualitative data, thematic analysis focuses on concepts, opinions, and experiences, as opposed to pure statistics.

A big advantage of thematic analysis is that it allows a lot of flexibility in terms of designing theoretical and research frameworks. It also supports the generation and interpretation of themes that are backed by data.

A disadvantage of thematic analysis is that it can be highly subjective and can overlook nuance in the data. Also, researchers must be aware of how their own position and perspective influences their interpretations of the data and if they are imposing meaning that is not there or failing to pick up on meaning that is.

How many themes make sense in your thematic analysis of course depends on your topic and the material you are working with. In general, it makes sense to have no more than 6-10 broader themes, instead of having many really detailed ones. You can then identify further nuances and differences under each theme when you are diving deeper into the topic.

Since thematic analysis is used to study qualitative data, it works best in cases where you’re looking to gather information about people’s views, values, opinions, experiences, and knowledge. Therefore, it makes sense to use thematic analysis for interviews.

After familiarizing yourself with your data, the first step of a thematic analysis is coding notable features of the data in a methodical way. This often means highlighting portions of the text and applying labels, aka codes, to them that describe the nature of their content.

Thematic Analysis

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• Virginia Braun 2 ,
• Victoria Clarke 3 ,
• Nikki Hayfield 3 &
• Gareth Terry 4  

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This chapter maps the terrain of thematic analysis (TA), a method for capturing patterns (“themes”) across qualitative datasets. We identify key concepts and different orientations and practices, illustrating why TA is often better understood as an umbrella term, used for sometimes quite different approaches, than a single qualitative analytic approach. Under the umbrella, three broad approaches can be identified: a “coding reliability” approach, a “codebook” approach, and a “reflexive” approach. These are often characterized by distinctive – sometimes radically different – conceptualizations of what a theme is, as well as methods for theme identification and development, and indeed coding. We then provide practical guidance on completing TA within our popular (reflexive) approach to TA, discussing each phase of the six-phase approach we have developed in relation to a project on men, rehabilitation, and embodiment. We conclude with a discussion of key concerns related to ensuring the TA you do – within whatever approach – is of the highest quality.

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Ando H, Cousins R, Young C. Achieving saturation in thematic analysis: development and refinement of a codebook. Compr Psychol. 2014;3:4.

Article   Google Scholar  

Aronson J. A pragmatic view of thematic analysis. Qual Rep. 1994;2(1):1–3. http://www.nova.edu/ssss/QR/BackIssues/QR2-1/aronson.html .

Google Scholar  

Boyatzis RE. Transforming qualitative information: thematic analysis and code development. Thousand Oaks: Sage; 1998.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

Braun V, Clarke V. Thematic analysis. In: Cooper H, editor. APA handbook of research methods in psychology, Research designs, vol. 2. Washington, DC: APA books; 2012. p. 57–71.

Braun V, Clarke V. Successful qualitative research: a practical guide for beginners. London: Sage; 2013.

Braun V, Clarke V. (Mis)conceptualising themes, thematic analysis, and other problems with Fugard and Potts’ (2015) sample-size tool for thematic analysis. Int J Soc Res Methodol. 2016;19(6):739–43.

Braun V, Clarke V, Rance N. How to use thematic analysis with interview data. In: Vossler A, Moller N, editors. The counselling and psychotherapy research handbook. London: Sage; 2014. p. 183–97.

Brewster ME, Velez BL, Mennicke A, Tebbe E. Voices from beyond: a thematic content analysis of transgender employees’ workplace experiences. Psychol Sex Orient Gend Divers. 2014;1(2): 159–69.

Brooks J, McCluskey S, Turley E, King N. The utility of template analysis in qualitative psychological research. Qual Res Psychol. 2015;12(2):202–22.

Cedervall Y, Åberg AC. Physical activity and implications on well-being in mild Alzheimer’s disease: a qualitative case study on two men with dementia and their spouses. Physiother Theory Pract. 2010;26(4):226–39.

Connelly LM, Peltzer JN. Underdeveloped themes in qualitative research: relationships with interviews and analysis. Clin Nurse Spec. 2016;30:51–7.

Dapkus MA. A thematic analysis of the experience of time. Personal Process Individ Differ. 1985;49(2):408–19.

DeSantis L, Ugarriza DN. The concept of theme as used in qualitative nursing research. West J Nurs Res. 2000;22(3):351–72.

Finlay L, Gough B, editors. Reflexivity: a practical guide for researchers in health and social sciences. Oxford: Blackwell Science; 2003.

Fugard AJB, Potts HWW. Supporting thinking on sample sizes for thematic analysis: a quantitative tool. Int J Soc Res Methodol. 2015;18(6):669–84.

Gershgoren L, Basevitch I, Filho E, Gershgoren A, Brill YS, Shink RJ, Tenebaum G. Expertise in soccer teams: a thematic enquiry into the role of shared mental models within team chemistry. Psychol Sport Exerc. 2016;24:128–39.

Gough B, Madill A. Subjectivity in psychological research: from problem to prospect. Psychol Methods. 2012;17(3):374–84.

Guest G, Bunce A, Johnson L. How many interviews are enough? An experiment with data saturation and variability. Field Methods. 2006;18(1):59–82.

Guest G, MacQueen KM, Namey EE. Applied thematic analysis. Thousand Oaks: Sage; 2012.

Book   Google Scholar  

Hennink MM, Kaiser BN, Marconi VC. Code saturation versus meaning saturation: how many interviews are enough? Qual Health Res. 2016;27(4):591–608.

Hill M, Welch V, Gadda A. Contested views of expertise in children’s care and permanence proceedings. J Soc Welf Fam Law. 2017;39(1):42–66.

Holton GJ. Thematic origins of scientific thought: Kepler to Einstein. Cambridge, MA: Harvard University Press; 1973.

Joffe H. Thematic analysis. In: Harper D, Thompson AR, editors. Qualitative methods in mental health and psychotherapy: a guide for students and practitioners. Chichester: Wiley; 2011. p. 209–23.

Chapter   Google Scholar  

Kidder LH, Fine M. Qualitative and quantitative methods: when stories converge. In: Mark MM, Shotland L, editors. New directions in program evaluation. San Francisco: Jossey-Bass; 1987. p. 57–75.

King N. Using templates in the thematic analysis of text. In: Cassell C, Symon G, editors. Essential guide to qualitative methods in organisational research. London: Sage; 2014. p. 256–70.

Kinsky G, Strunk WO. Was Mendelssohn indebted to weber? An attempted solution of an old controversy. Music Q. 1933;19(2):178–86.

Langdridge D. Introduction to research methods and data analysis in psychology. Harlow: Pearson; 2004.

Lazarsfeld PF, Merton RK. The psychological analysis of propaganda. In: Proceedings, Writers’ Congress: Berkeley and Los Angeles: University of California Press; 1944.

Madill AL. Qualitative research is not a paradigm. Qual Psychol. 2015;2(2):214–20.

Malterud K, Siersma VK, Guassora AD. Sample size in qualitative interview studies: guided by information power. Qual Health Res. 2016;26(13):1753–60.

Manago AM. Negotiating a sexy masculinity on social networking sites. Fem Psychol. 2013;23(4):478–97.

McSherry W, Bloomfield S, Thompson R, Nixon VA, Birch C, Griffiths N, Fisher S, Boughey AJ. A cross-sectional analysis of the factors that shape adult nursing students’ values, attitudes and perceptions of compassionate care. J Res Nurs. 2017;22(1–2):25–39.

Mellor R, Lovell A. The lived experience of UK street-based sex workers and the health consequences: an exploratory study. Health Promot Int. 2012;27(3):311–22.

Miles MB, Huberman AM. Qualitative data analysis: an expanded sourcebook. 2nd ed. Thousand Oaks: Sage; 1994.

Moran C, Lee C. Selling genital cosmetic surgery to healthy women: a multimodal discourse analysis of Australian surgical websites. Crit Discourse Stud. 2013;10(4):373–91.

Nadin S, Cassell C. Using data matrices. In: Cassell C, Symon G, editors. Essential guide to qualitative methods in organisational research. London: Sage; 2014. p. 271–87.

Namey E, Guest G, McKenna K, Chen M. Evaluating bang for the buck: a cost-effectiveness comparison between individual interviews and focus groups based on thematic saturation levels. Am J Eval. 2016;37(3):425–40.

Redmond L, Suddick K, Earhart GM. The lived experience of freezing in people with Parkinson’s: an interpretive phenomenological approach. Int J Ther Rehabil. 2012;19(3):169–77.

Ritchie J, Lewis J. Qualitative research practice: a guide for social science students and researchers. London: Sage; 2003.

Ritchie J, Spencer L. Qualitative data analysis for applied policy research. In: Bryman A, Burgess RG, editors. Analysing qualitative data. London: Taylor & Francis; 1994. p. 173–94.

Roditis ML, Halpern-Felsher B. Adolescents’ perceptions of risks and benefits of conventional cigarettes, e-cigarettes, and marijuana: a qualitative analysis. J Adolesc Health. 2015;57:179–85.

Sandelowski M, Leeman J. Writing usable qualitative health research findings. Qual Health Res. 2012;22(10):1404–13.

Smith J, Firth J. Qualitative data approaches: the framework approach. Nurs Res. 2011;18(2): 52–62.

Terry D, Ali M, Lê Q. Asian migrants’ lived experience and acculturation to Western health care in rural Tasmania. Aust J Rural Health. 2011;19(6):318–23.

Terry G, Braun V. “I think gorilla-like back effusions of hair are rather a turn-off”: ‘excessive hair’ and male body hair (removal) discourse. Body Image. 2016;17:14–24.

Terry G, Hayfield N, Clarke V, Braun V. Thematic analysis. In: Willig C, Stainton Rogers W, editors. The sage handbook of qualitative research in psychology. 2nd ed. London: Sage; 2017. p. 17–37.

Titlestad A, Pooley JA. Resilience in same-sex-parented families: the lived experience of adults with gay, lesbian, or bisexual parents. J GLBT Fam Stud. 2014;10(4):329–53.

Weatherhead S, Daiches A. Muslim views on mental health and psychotherapy. Psychology and Psychotherapy: Theory, Research and Practice. 2010;83(1):75–89.

Willig C. Introducing qualitative research in psychology: Adventures in theory and method. 3rd ed. Maidenhead, Berkshire: McGraw-Hill Education (UK); 2013.

Winder AE, Hersko M. A thematic analysis of an outpatient psychotherapy group. Int J Group Psychother. 1958;8(3):293–300.

Yardley L. Demonstrating validity in qualitative psychology. In: Smith JA, editor. Qualitative psychology: a practical guide to research methods. 3rd ed. London: Sage; 2015. p. 257–72.

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Thematic Analysis: What it is and How to Do It

Qualitative analysis may be a highly effective analytical approach when done correctly. Thematic analysis is one of the most frequently used qualitative analysis approaches.

One advantage of this analysis is that it is a versatile technique that can be utilized for both exploratory research (where you don’t know what patterns to look for) and more deductive studies (where you see what you’re searching for).

LEARN ABOUT:  Research Process Steps

This article will break it down and show you how to do the thematic analysis correctly.

What is thematic analysis?

Thematic analysis is a method for analyzing qualitative data that involves reading through a set of data and looking for patterns in the meaning of the data to find themes. It is an active process of reflexivity in which the researcher’s subjective experience is at the center of making sense of the data.

LEARN ABOUT: Qualitative Interview

Thematic analysis is typical in qualitative research. It emphasizes identifying, analyzing, and interpreting qualitative data patterns.

With this analysis, you can look at qualitative data in a certain way. It is usually used to describe a group of texts, like an interview or a set of transcripts. The researcher looks closely at the data to find common themes: repeated ideas, topics, or ways of putting things.

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Thematic Analysis Advantages and Disadvantages

A technical or pragmatic view of research design focuses on researchers conducting qualitative analyzes using the method most appropriate to the research question. However, there is seldom a single ideal or suitable method, so other criteria are often used to select methods of analysis: the researcher’s theoretical commitments and familiarity with particular techniques.

The thematic analysis provides a flexible method of data analysis and allows researchers with diverse methodological backgrounds to participate in this type of analysis. Data analytics and data analysis are closely related processes that involve extracting insights from data to make informed decisions.

For positivists, ‘reliability’ is a concern because of the many possible interpretations of the data and the potential for researcher subjectivity to ‘bias’ or distort the analysis. For those committed to the values ​​of steps in qualitative research , researcher subjectivity is seen as a resource (rather than a threat to credibility), so concerns about reliability do not remain.

There is no correct or precise interpretation of the data. The interpretations are inevitably subjective and reflect the position of the researcher. Quality is achieved through a systematic and rigorous approach and the researcher’s continual reflection on how they shape the developing analysis.

Thematic analysis has several advantages and disadvantages. It is up to the researchers to decide if this analysis method is suitable for their research design.

• The flexibility of theoretical and research design allows researchers multiple theories that can be applied to this process in various epistemologies.
• Very suitable for large data sets.
• The coding and codebook reliability approaches are designed for use with research teams.
• Interpretation of themes supported by data.
• Applicable to research questions that go beyond the experience of an individual.
• It allows the inductive development of codes and themes from data.

Disadvantages

• Thematic analysis can miss nuanced data if the researcher is not careful and uses thematic analysis in a theoretical vacuum.
• The flexibility can make it difficult for novice researchers to decide which aspects of the data to focus on.
• Limited interpretive power if the analysis is not based on a theoretical framework.
• It is challenging to maintain a sense of data continuity across individual accounts due to the focus on identifying themes across all data elements.
• Unlike discourse analysis and narrative analysis, it does not allow researchers to make technical claims about language use.

LEARN ABOUT: Level of Analysis

Thematic Analysis Steps

Let’s jump right into the process of thematic analysis. Remember that what we’ll talk about here is a general process, and the steps you need to take will depend on your approach and the research design .

1. Familiarization

The first stage in thematic analysis is examining your data for broad themes. This is where you transcribe audio data to text.

At this stage, you’ll need to decide what to code, what to employ, and which codes best represent your content. Now consider your topic’s emphasis and goals.

Keep a reflexivity diary. You’ll explain how you coded the data, why, and the results here. You may reflect on the coding process and examine if your codes and themes support your results. Using a reflective notebook from the start can help you in the later phases of your analysis.

A reflexivity journal increases dependability by allowing systematic, consistent data analysis . If using a reflexivity journal, specify your starting codes to see what your data reflects. Later on, the coded data may be analyzed more extensively or may find separate codes.

2. Look for themes in the codes.

At this stage, search for coding patterns or themes. From codes to themes is not a smooth or straightforward process. You may need to assign alternative codes or themes to learn more about the data.

As you analyze the data, you may uncover subthemes and subdivisions of themes that concentrate on a significant or relevant component. At this point, your reflexivity diary entries should indicate how codes were understood and integrated to produce themes.

3. Review themes

Now that you know your codes, themes, and subthemes. Evaluate your topics. At this stage, you’ll verify that everything you’ve classified as a theme matches the data and whether it exists in the data. If any themes are missing, you can continue to the next step, knowing you’ve coded all your themes properly and thoroughly.

If your topics are too broad and there’s too much material under each one, you may want to separate them so you can be more particular with your research .

In your reflexivity journal, please explain how you comprehended the themes, how they’re backed by evidence, and how they connect with your codes. You should also evaluate your research questions to ensure the facts and topics you’ve uncovered are relevant.

4. Finalize Themes

Your analysis will take shape now after reviewing and refining your themes, labeling, and finishing them. Just because you’ve moved on doesn’t mean you can’t edit or rethink your topics. Finalizing your themes requires explaining them in-depth, unlike the previous phase. Whether you have trouble, check your data and code to see if they reflect the themes and whenever you need to split them into multiple pieces.

Make sure your theme name appropriately describes its features.

Ensure your themes match your research questions at this point. When refining, you’re reaching the end of your analysis. You must remember that your final report (covered in the following phase) must meet your research’s goals and objectives.

In your reflexivity journal, explain how you choose your topics. Mention how the theme will affect your research results and what it implies for your research questions and emphasis.

By the conclusion of this stage, you’ll have finished your topics and be able to write a report.

5. Report writing

At this stage, you are nearly done! Now that you’ve examined your data write a report. A thematic analysis report includes:

• An approach
• The results

When drafting your report, provide enough details for a client to assess your findings. In other words, the viewer wants to know how you analyzed the data and why. “What”, “how”, “why”, “who”, and “when” are helpful here.

So, what did you find? What did you do? How did you choose this method? Who are your research’s focus and participants? When were your studies, data collection , and data production? Your reflexivity notebook will help you name, explain, and support your topics.

While writing up your results, you must identify every single one. The reader needs to be able to verify your findings. Make sure to relate your results to your research questions when reporting them. Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders. You don’t want your client to wonder about your results, so make sure they’re related to your subject and queries.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Because it is easy to apply, thematic analysis suits beginner researchers unfamiliar with more complicated qualitative research . It permits the researcher to choose a theoretical framework with freedom.

The versatility of thematic analysis enables you to describe your data in a rich, intricate, and sophisticated way. This technique may be utilized with whatever theory the researcher chooses, unlike other methods of analysis that are firmly bound to specific approaches. These steps can be followed to master proper thematic analysis for research.

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Thematic analysis in qualitative research.

11 min read Your guide to thematic analysis, a form of qualitative research data analysis used to identify patterns in text, video and audio data.

What is thematic analysis?

Thematic analysis is used to analyze qualitative data – that is, data relating to opinions, thoughts, feelings and other descriptive information. It’s become increasingly popular in social sciences research, as it allows researchers to look at a data set containing multiple qualitative sources and pull out the broad themes running through the entire data set.

That data might consist of articles, diaries, blog posts, interview transcripts, academic research, web pages, social media and even audio and video files. They are put through data analysis as a group, with researchers seeking to identify patterns running through the corpus as a whole.

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Thematic analysis steps

Image source: https://www.nngroup.com/articles/thematic-analysis/

While there are many types of thematic analysis, the thematic analysis process can be generalized into six steps. Thematic analysis involves initial analysis, coding data, identifying themes and reporting on the findings.

• Familiarization – During the first stage of thematic analysis, the research teams or researchers become familiar with the dataset. This may involve reading and re-reading, and even transcribing the data. Researchers may note down initial thoughts about the potential themes they perceive in the data, which can be the starting point for assigning initial codes.
• Coding – Codes in thematic analysis are the method researchers use to identify the ideas and topics in their data and refer to them quickly and easily. Codes can be assigned to snippets of text data or clips from videos and audio files. Depending on the type of thematic analysis used, this can be done with a systematic and rigorous approach, or in a more intuitive manner.
• Identifying theme – Themes are the overarching ideas and subject areas within the corpus of research data. Researchers can identify themes by collating together the results of the coding process, generating themes that tie together the identified codes into groups according to their meaning or subject matter.
• Reviewing themes – Once the themes have been defined, the researchers check back to see how well the themes support the coded data extracts. At this stage they may start to organize the themes into a map, or early theoretical framework.
• Defining and naming themes – As researchers spend more time reviewing the themes, they begin to define them more precisely, giving them names. Themes are different from codes, because they capture patterns in the data rather than just topics, and they relate directly to the research question.
• Writing up – At this stage, researchers begin to develop the final report, which offers a comprehensive summary of the codes and themes, extracts from the original data that illustrate the findings, and any other data relevant to the analysis. The final report may include a literature review citing other previous research and the observations that helped frame the research question. It can also suggest areas for future research the themes support, and which have come to light during the research process.

Another step which precedes all of these is data collection. Common to almost all forms of qualitative analysis, data collection means bringing together the materials that will be part of the data set, either by finding secondary data or generating first-party data through interviews, surveys and other qualitative methods.

Types of thematic analysis

There are various thematic analysis approaches currently in use. For the most part, they can be viewed as a continuum between two different ideologies. Reflexive thematic analysis (RTA) sits at one end of the continuum of thematic analysis methods. At the other end is code reliability analysis.

Code reliability analysis emphasizes the importance of the codes given to themes in the research data being as accurate as possible. It takes a technical or pragmatic view, and places value on codes being replicable between different researchers during the coding process. Codes are based on domain summaries, which often link back to the questions in a structured research interview.

Researchers using a code reliability approach may use a codebook. A codebook is a detailed list of codes and their definitions, with exclusions and examples of how the codes should be applied.

Reflexive thematic analysis was developed by Braun & Clarke in 2006 for use in the psychology field. In contrast to code reliability analysis, it isn’t concerned with consistent codes that are agreed between researchers. Instead, it acknowledges and finds value in each researcher’s interpretation of the thematic content and how it influences the coding process. The codes they assign are specific to them and exist within a unique context that is made up of:

• The data set
• The assumptions made during the setup of the analysis process
• The researcher’s skills and resources

This doesn’t mean that reflexive thematic analysis should be unintelligible to anyone other than the researcher. It means that the researcher’s personal subjectivity and uniqueness is made part of the process, and is expected to have an influence on the findings. Reflexive thematic analysis is a flexible method, and initial codes may change during the process as the researcher’s understanding evolves.

Reflexive thematic analysis is an inductive approach to qualitative research. With an inductive approach, the final analysis is based entirely on the data set itself, rather than from any preconceived themes or structures from the research team.

Image source: https://delvetool.com/blog/thematicanalysis

Thematic analysis vs other qualitative research methods

Thematic analysis sits within a whole range of qualitative analysis methods which can be applied to social sciences, psychology and market research data.

• Thematic analysis vs comparative analysis – Comparative analysis and thematic analysis are closely related, since they both look at relationships between multiple data sources. Comparative analysis is a form of qualitative research that works with a smaller number of data sources. It focuses on causal relationships between events and outcomes in different cases, rather than on defining themes.
• Thematic analysis vs discourse analysis – Unlike discourse analysis, which is a type of qualitative research that focuses on spoken or written conversational language, thematic analysis is much more broad in scope, covering many kinds of qualitative data.
• Thematic analysis vs narrative analysis – Narrative analysis works with stories – it aims to keep information in a narrative structure, rather than allowing it to be fragmented, and often to study the stories from participants’ lives. Thematic analysis can break narratives up as it allocates codes to different parts of a data source, meaning that the narrative context might be lost and even that researchers might miss nuanced data.
• Thematic analysis vs content analysis – Both content analysis and thematic analysis use data coding and themes to find patterns in data. However, thematic analysis is always qualitative, but researchers agree there can be quantitative and qualitative content analysis, with numerical approaches to the frequency of codes in content analysis data.

Thematic analysis advantages and disadvantages

Like any kind of qualitative analysis, thematic analysis has strengths and weaknesses. Whether it’s right for you and your research project will depend on your priorities and preferences.

Thematic analysis advantages

• Easy to learn – Whether done manually or assisted by technology, the thematic analysis process is easy to understand and conduct, without the need for advanced statistical knowledge
• Flexible – Thematic analysis allows qualitative researchers flexibility throughout the process, particularly if they opt for reflexive thematic analysis
• Broadly applicable – Thematic analysis can be used to address a wide range of research questions.

Thematic analysis – the cons

As well as the benefits, there are some disadvantages thematic analysis brings up.

• Broad scope – In identifying patterns on a broad scale, researchers may become overwhelmed with the volume of potential themes, and miss outlier topics and more nuanced data that is important to the research question.
• Themes or codes? – It can be difficult for novice researchers to feel confident about the difference between themes and codes
• Language barriers – Thematic analysis relies on language-based codes that may be difficult to apply in multilingual data sets, especially if the researcher and / or research team only speaks one language.

How can you use thematic analysis for business research?

Thematic analysis, and other forms of qualitative research, are highly valuable to businesses who want to develop a deeper understanding of the people they serve, as well as the people they employ. Thematic analysis can help your business get to the ‘why’ behind the numerical information you get from quantitative research.

An easy way to think about the interplay between qualitative data and quantitative data is to consider product reviews. These typically include quantitative data in the form of scores (like ratings of up to 5 stars) plus the explanation of the score written in a customer’s own words. The word part is the qualitative data. The scores can tell you what is happening – lots of 3 star reviews indicate there’s some room for improvement for example – but you need the addition of the qualitative data, the review itself, to find out what’s going on.

Qualitative data is rich in information but hard to process manually. To do qualitative research at scale, you need methods like thematic analysis to get to the essence of what people think and feel without having to read and remember every single comment.

Qualitative analysis is one of the ways businesses are borrowing from the world of academic research, notably social sciences, statistical data analysis and psychology, to gain an advantage in their markets.

Analyzing themes across video, text, audio and more

Carrying out thematic analysis manually may be time-consuming and painstaking work, even with a large research team. Fortunately, machine learning and other technologies are now being applied to data analysis of all kinds, including thematic analysis, taking the manual work out of some of the more laborious thematic analysis steps.

The latest iterations of machine learning tools are able not only to analyze text data, but to perform efficient analysis of video and audio files, matching the qualitative coding and even helping build out the thematic map, while respecting the researcher’s theoretical commitments and research design.

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Chapter 22: Thematic Analysis

Darshini Ayton

Learning outcomes

Upon completion of this chapter, you should be able to:

• Describe the different approaches to thematic analysis.
• Understand how to conduct the three types of thematic analysis.
• Identify the strengths and limitations of each type of thematic analysis.

What is thematic analysis?

Thematic analysis is a common method used in the analysis of qualitative data to identify, analyse and interpret meaning through a systematic process of generating codes (see Chapter 20) that leads to the development of themes. 1 Thematic analysis requires the active engagement of the researcher with the data, in a process of sorting, categorising and interpretation. 1 Thematic analysis is exploratory analysis whereby codes are not predetermined and are data-derived, usually from primary sources of data (e,g, interviews and focus groups). This is in contrast to themes generated through directed or summative content analysis, which is considered confirmatory hypothesis-driven analysis, with predetermined codes typically generated from a hypothesis (see Chapter 21). 2 There are many forms of thematic analysis. Hence, it is important to treat thematic analysis as one of many methods of analysis, and to justify the approach on the basis of the research question and pragmatic considerations such as resources, time and audience. The three main forms of thematic analysis used in health and social care research, discussed in this chapter, are:

Applied thematic analysis

• Framework analysis
• Reflexive thematic analysis.

This involves multiple, inductive analytic techniques designed to identify and examine themes from textual data in a way that is transparent and credible, drawing from a broad range of theoretical and methodological perspectives. It focuses on presenting the stories of participants as accurately and comprehensively as possible. Applied thematic analysis mixes a bit of everything: grounded theory, positivism, interpretivism and phenomenology. 2

Applied thematic analysis borrows what we feel are the more useful techniques from each theoretical and methodological camp and adapts them to an applied research context. 2(p16)

Applied thematic analysis involves five elements:

• Text s egmentation  involves identifying a meaningful segment of text and the boundaries of the segment. Text segmentation is a useful process as a transcript from a 30-minute interview can be many pages long. Hence, segmenting the text provides a manageable section of the data for interrogation of meaning. For example, text segmentation may be a participant’s response to an interview question, a keyword or concept in context, or a complete discourse between participants. The segment of text is more than a short phrase and can be both small and large sections of text. Text segments can also overlap, and a smaller segment may be embedded within a larger segment. 3
• Creation of the codebook is a critical element of applied thematic analysis. The codebook is created when the segments of text are systematically coded into categories, types and relationships, and the codes are defined by the observed meaning in the text. The codes and their definitions are descriptive in the beginning, and then evolve into explanatory codes as the researcher examines the commonalities, differences and relationships between the codes. The codebook is an iterative document that the researcher builds and refines as they become more immersed and familiar with the data. 3 Table 22.1 outlines the key components of a codebook. 3

Table 22.1. Codebook components and an example

• Structural coding can be useful if a structured interview guide or focus group guide has been used by the researcher and the researcher stays close to the wording of the question and its prompts. The structured question is the structural code in the codebook, and the text segment should include the participant’s response and any dialogue following the question. Of course, this form of coding can be used even if the researcher does not follow a structured guide, which is often the reality of qualitative data collection. The relevant text segments are coded for the specific structure, as appropriate. 3
• Content coding is informed by the research question(s) and the questions informing the analysis. The segmented text is grouped in different ways to explore relationships, hierarchies, descriptions and explanations of events, similarities, differences and consequences. The content of the text segment should be read and re-read to identify patterns and meaning, with the generated codes added to the codebook.
• Themes vary in scope, yet at the core they are phrases or statements that explain the meaning of the text. Researchers need to be aware that themes are considered a higher conceptual level than codes, and therefore should not be comprised of single words or labels. Typically, multiple codes will lead to a theme. Revisiting the research and analysis questions will assist the researcher to identify themes. Through the coding process, the researcher actively searches the data for themes. Examples of how themes may be identified include the repetition of concepts within and across transcripts, the use of metaphors and analogies, key phrases and common phrases used in an unfamiliar way. 3

Framework a nalysis

This method originated in the 1980s in social policy research. Framework analysis is suited to research seeking to answer specific questions about a problem or issue, within a limited time frame and with homogenous data (in topics, concepts and participants); multiple researchers are usually involved in the coding process. 4-6 The process of framework analysis is methodical and suits large data sets, hence is attractive to quantitative researchers and health services researchers. Framework analysis is useful for multidisciplinary teams in which not all members are familiar with qualitative analysis. Framework analysis does not seek to generate theory and is not aligned with any particular epistemological, philosophical or theoretical approach. 5 The output of framework analysis is a matrix with rows (cases), columns (codes) and cells of summarised data that enables researchers to analyse the data case by case and code by code. The case is usually an individual interview, or it can be a defined group or organisation. 5

The process for conducting framework analysis is as follows 5 :

1. Transcription – usually verbatim transcription of the interview.

2. Familiarisation with the interview – reading the transcript and listening to the audio recording (particularly if the researcher doing the analysis did not conduct the interview) can assist in the interpretation of the data. Notes on analytical observations, thoughts and impressions are made in the margins of the transcript during this stage.

3. Coding – completed in a line-by-line method by at least two researchers from different disciplines (or with a patient or public involvement representative), where possible. Coding can be both deductive – (using a theory or specific topics relevant to the project – or inductive, whereby open coding is applied to elements such as behaviours, incidents, values, attitudes, beliefs, emotions and participant reactions. All data is coded.

4. Developing a working analytical framework – codes are collated and organised into categories, to create a structure for summarising or reducing the data.

5. Applying the analytical framework – indexing the remaining transcripts by using the categories and codes of the analytical framework.

6. Charting data into the framework matrix – summarising the data by category and from each transcript into the framework matrix, which is a spreadsheet with numbered cells in which summarised data are entered by codes (columns) and cases (rows). Charting needs to balance the reduction of data to a manageable few lines and retention of the meaning and ‘feel’ of the participant. References to illustrative quotes should be included.

7. Interpreting the data – using the framework matrix and notes taken throughout the analysis process to interpret meaning, in collaboration with team members, including lay and clinical members.

Reflexive thematic analysis

This is the thematic analysis approach developed by Braun and Clarke in 2006 and explained in the highly cited article ‘ Using thematic analysis in psychology ’ . 7 Reflexive thematic analysis recognises the subjectiveness of the analysis process, and that codes and themes are actively generated by the researcher. Hence, themes and codes are influenced by the researcher’s values, skills and experiences. 8 Reflexive thematic analysis ‘exists at the intersection of the researcher, the dataset and the various contexts of interpretation’. 9(line 5-6) In this method, the coding process is less structured and more organic than in applied thematic analysis. Braun and Clarke have been critical of the use of the term ‘emerging themes’, which many researchers use to indicate that the theme was data-driven, as opposed to a deductive approach:

This language suggests that meaning is self evident and somehow ‘within’ the data waiting to be revealed, and that the researcher is a neutral conduit for the revelation of said meaning. In contrast, we conceptualise analysis as a situated and interactive process, reflecting both the data, the positionality of the researcher, and the context of the research itself… it is disingenuous to evoke a process whereby themes simply emerge, instead of being active co-productions on the part of the researcher, the data/participants and context. 10 (p15)

Since 2006, Braun and Clarke have published extensively on reflexive thematic analysis, including a methodological paper comparing reflexive thematic analysis with other approaches to qualitative analysis, 8 and have provided resources on their website to support researchers and students. 9 There are many ways to conduct reflexive thematic analysis, but the six main steps in the method are outlined following. 9 Note that this is not a linear, prescriptive or rule-based process, but rather an approach to guide researchers in systematically and robustly exploring their data.

1.  Familiarisation with data – involves reading and re-reading transcripts so that the researcher is immersed in the data. The researcher makes notes on their initial observations, interpretations and insights for both the individual transcripts and across all the transcripts or data sources.

2.  Coding – the process of applying succinct labels (codes) to the data in a way that captures the meaning and characteristics of the data relevant to the research question. The entire data set is coded in numerous rounds; however, unlike line-by-line coding in grounded theory (Chapter 27), or data segmentation in applied thematic analysis, not all sections of data need to be coded. 8 After a few rounds of coding, the codes are collated and relevant data is extracted.

3.  Generating initial themes – using the collated codes and extracted data, the researcher identifies patterns of meaning (initial or potential themes). The researcher then revisits codes and the data to extract relevant data for the initial themes, to examine the viability of the theme.

4 .  Developing and reviewing themes – checking the initial themes against codes and the entire data set to assess whether it captures the ‘story’ of the data and addresses the research question. During this step, the themes are often reworked by combining, splitting or discarding. For reflexive thematic analysis, a theme is defined as a ‘pattern of shared meaning underpinned by a central concept or idea’. 8 (p 39 )

5.  Refining, defining and naming themes – developing the scope and boundaries of the theme, creating the story of the theme and applying an informative name for the theme.

6.  Writing up – is a key part of the analysis and involves writing the narrative of the themes, embedding the data and providing the contextual basis for the themes in the literature.

Themes versus c odes

As described above, themes are informed by codes, and themes are defined at a conceptually higher level than codes. Themes are broader categorisations that tend to describe or explain the topic or concept. Themes need to extend beyond the code and are typically statements that can stand alone to describe and/or explain the data. Fereday and Muir-Cochrane explain this development from code to theme in Table 22.2. 11

Table 22.2. Corroborating and legitimating coded themes to identify second-order themes

*Note: This table is from an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

When I [the author] first started publishing qualitative research, many of my themes were at the code level. I then got advice that when the themes are the subheadings of the results section of my paper, they should tell the story of the research. The difference in my theme naming can be seen when comparing a paper from my PhD thesis, 12 which explores the challenges of church-based health promotion, with a more recent paper that I published on antimicrobial stewardship 13 (refer to the theme tables in the publications).

Table 22.3. Examples of thematic analysis

Advantages and challenges of thematic analysis.

Thematic analysis is flexible and can be used to analyse small and large data sets with homogenous and heterogenous samples. Thematic analysis can be applied to any type of data source, from interviews and focus groups to diary entries and online discussion forums. 1 Applied thematic analysis and framework analysis are accessible approaches for non-qualitative researchers or beginner researchers. However, the flexibility and accessibility of thematic analysis can lead to limitations and challenges when thematic analysis is misapplied or done poorly. Thematic analysis can be more descriptive than interpretive if not properly anchored in a theoretical framework. 1 For framework analysis, the spreadsheet matrix output can lead to quantitative researchers inappropriately quantifying the qualitative data. Therefore, training and support from a qualitative researcher with the appropriate expertise can help to ensure that the interpretation of the data is meaningful. 5

Thematic analysis is a family of analysis techniques that are flexible and inductive and involve the generation of codes and themes. There are three main types of thematic analysis: applied thematic analysis, framework analysis and reflexive thematic analysis. These approaches span from structured coding to organic and unstructured coding for theme development. The choice of approach should be guided by the research question, the research design and the available resources and skills of the researcher and team.

• Clarke V, Braun V. Thematic analysis. J Posit Psychol . 2017;12(3):297-298. doi:10.1080/17439760.2016.1262613
• Guest G, MacQueen KM, Namey EE. Introduction to applied thematic analysis. In: Guest G, MacQueen, K.M., Namey, E.E., ed. Applied thematic analysis . SAGE Publications, Inc.; 2014. Accessed September 18, 2023. https://methods.sagepub.com/book/applied-thematic-analysis
• Guest G, MacQueen, K.M., Namey, E.E.,. Themes and Codes. In: Guest G, MacQueen, K.M., Namey, E.E., ed. Applied thematic analysis . SAGE Publications, Inc.; 2014. Accessed September 18, 2023. https://methods.sagepub.com/book/applied-thematic-analysis
• Srivastava A, Thomson SB. Framework analysis: A qualitative methodology for applied policy research. Journal of Administration and Governance . 2009;72(3). Accessed September 14, 2023. https://ssrn.com/abstract=2760705
• Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol . 2013;13:117. doi:10.1186/1471-2288-13-117
• Smith J, Firth J. Qualitative data analysis: the framework approach. Nurse Res . 2011;18(2):52-62. doi:10.7748/nr2011.01.18.2.52.c8284
• Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol . 2006;3(2):77-101. doi:10.1191/1478088706qp063oa
• Braun V, Clarke V. Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern-based qualitative analytic approaches. Couns Psychother Res . 2021;21(1):37-47. doi:10.1002/capr.12360
• Braun V, Clarke V. Thematic analysis. University of Auckland. Accessed September 18, 2023. https://www.thematicanalysis.net/
• Braun V, Clarke V. Answers to frequently asked questions about thematic analysis. University of Auckland. Accessed September 18, 2023. https://www.thematicanalysis.net/faqs/
• Fereday J, Muir-Cochrane E. Demonstrating Rigour Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. International Journal of Qualitative Methods . 2006;5(1):80-92. doi: 10.1177/160940690600500107
• Ayton D, Manderson L, Smith BJ. Barriers and challenges affecting the contemporary church’s engagement in health promotion. Health Promot J Austr . 2017;28(1):52-58. doi:10.1071/HE15037
• Ayton D, Watson E, Betts JM, et al. Implementation of an antimicrobial stewardship program in the Australian private hospital system: qualitative study of attitudes to antimicrobial resistance and antimicrobial stewardship. BMC Health Serv Res . 2022;22(1):1554. doi:10.1186/s12913-022-08938-8
• McKenna-Plumley PE, Graham-Wisener L, Berry E, Groarke JM. Connection, constraint, and coping: A qualitative study of experiences of loneliness during the COVID-19 lockdown in the UK. PLoS One . 2021;16(10):e0258344. doi:10.1371/journal.pone.0258344
• Dickinson BL, Gibson K, VanDerKolk K, et al. “It is this very knowledge that makes us doctors”: an applied thematic analysis of how medical students perceive the relevance of biomedical science knowledge to clinical medicine. BMC Med Educ . 2020;20(1):356. doi:10.1186/s12909-020-02251-w
• Bunzli S, O’Brien P, Ayton D, et al. Misconceptions and the acceptance of evidence-based nonsurgical interventions for knee osteoarthritis. A Qualitative Study. Clin Orthop Relat Res . 2019;477(9):1975-1983. doi:10.1097/CORR.0000000000000784

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Darshini Ayton is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Thematic analysis of qualitative data: AMEE Guide No. 131

Affiliations.

• 1 Wright-Patterson Medical Center, Dayton, OH, USA.
• 2 Uniformed Services University of the Healthy Sciences, Bethesda, MD, USA.
• PMID: 32356468
• DOI: 10.1080/0142159X.2020.1755030

Thematic analysis is a widely used, yet often misunderstood, method of qualitative data analysis. It is a useful and accessible tool for qualitative researchers, but confusion regarding the method's philosophical underpinnings and imprecision in how it has been described have complicated its use and acceptance among researchers. In this Guide, we outline what thematic analysis is, positioning it in relation to other methods of qualitative analysis, and describe when it is appropriate to use the method under a variety of epistemological frameworks. We also provide a detailed definition of a theme , as this term is often misapplied. Next, we describe the most commonly used six-step framework for conducting thematic analysis, illustrating each step using examples from our own research. Finally, we discuss advantages and disadvantages of this method and alert researchers to pitfalls to avoid when using thematic analysis. We aim to highlight thematic analysis as a powerful and flexible method of qualitative analysis and to empower researchers at all levels of experience to conduct thematic analysis in rigorous and thoughtful way.

Keywords: Thematic analysis; qualitative analysis; qualitative research methods.

• Qualitative Research
• Research Design*

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Original research

Silver linings of adhd: a thematic analysis of adults’ positive experiences with living with adhd, emilie s. nordby.

1 Division of Psychiatry, Haukeland University Hospital, Bergen, Norway

2 Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway

Frode Guribye

3 Department of Information Science and Media Studies, University of Bergen, Bergen, Norway

Tine Nordgreen

4 Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway

Astri J. Lundervold

Associated data.

Data are available upon reasonable request. The data is available from the corresponding author upon reasonable request.

To identify and explore positive aspects of attention deficit hyperactivity disorder (ADHD) as reported by adults with the diagnosis.

The current study used a qualitative survey design including the written responses to an open-ended question on positive aspects of ADHD. The participants’ responses were analysed using thematic analysis.

The participants took part in trial of a self-guided internet-delivered intervention in Norway. As part of the intervention, the participants were asked to describe positive aspects of having ADHD.

Participants

The study included 50 help-seeking adults with an ADHD diagnosis.

The participants described a variety of positive aspects related to having ADHD. The participants’ experiences were conceptualised and thematically organised into four main themes: (1) the dual impact of ADHD characteristics; (2) the unconventional mind; (3) the pursuit of new experiences and (4) resilience and growth.

Conclusions

Having ADHD was experienced as both challenging and beneficial, depending on the context and one’s sociocultural environment. The findings provide arguments for putting a stronger emphasis on positive aspects of ADHD, alongside the challenges, in treatment settings.

Trial registration number

{"type":"clinical-trial","attrs":{"text":"NCT04511169","term_id":"NCT04511169"}} NCT04511169

STRENGTHS AND LIMITATIONS OF THIS STUDY

• The current study is one of few studies that focuses on positive aspects of attention deficit hyperactivity disorder (ADHD).
• With the current study design, we could only explore the participants’ experiences with positive aspects of ADHD. Future studies are needed to examine the generalisability of these positive aspects.
• The large majority of the sample were women, which makes the findings less transferable to men.
• The sample is restricted to including participants who responded to a question regarding positive traits.

Introduction

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder with prevalence estimates of approximately 5% of children and 2.6% of adults. 1 2 Recently, the number of individuals being diagnosed with ADHD in adulthood has increased, with women being at particular risk for receiving a diagnosis later in life. 3 4 The diagnosis of ADHD is characterised by three cardinal symptoms: inattention, hyperactivity and impulsivity. 5 Adults with ADHD also tend to face additional challenges related to emotion dysregulation, poor working memory, planning and organisation skills. 6 7 These symptoms and challenges are known to interfere with many activities of daily living, with impact on occupational, educational, interpersonal and financial domains. 5 Pharmacological treatment is the primary treatment for adults with ADHD, but many seek additional psychological treatment. 8 9

Research on ADHD has traditionally focused on the impairments and negative outcomes associated with the diagnosis. When portraying a primarily deficit-oriented view on the diagnosis, it may add to the burden of living with ADHD. For instance, it is well known that individuals with ADHD are prone to experience public stigma, prejudice and criticism based on their diagnosis, which can negatively impact self-esteem, self-efficacy and well-being. 10 11 Moreover, a deficit-oriented view of ADHD may overlook strengths of persons with the diagnosis. An alternative approach would be to adopt a more ability-oriented view of ADHD, emphasising the individuals’ resources, abilities and skills. 12 This perspective aligns with beliefs of the neurodiversity movement, which has gained considerable recognition with the rise of social media platforms like TikTok and Instagram. 13 Unlike the biological-medical perspective of ADHD, the neurodiversity movement advocates that ADHD and similar conditions should be denoted as neurological differences rather than being conceptualised as deficits. 14 15 From this perspective, the neurological differences associated with ADHD are considered to be of societal benefit as they contribute to valuable diversity within the population. 15

The majority of available studies on psychological treatment interventions for adults with ADHD also tend to have a deficit-oriented focus, with most studies defining successful treatment as reduction in core ADHD symptoms. 16 Among 23 studies on cognitive behavioural therapy for adults with ADHD included in a recent systematic review, only one study examined an intervention with an explicit focus on strengths associated with having ADHD. 17 18 This particular intervention was found to improve the participants’ knowledge about ADHD and life satisfaction, giving support for incorporation of a strength-based approach in psychological treatment for adults with ADHD. 17 Findings from qualitative research further indicate that public mental healthcare is perceived as too deficit centred and symptom centred by adults with ADHD, leading some to seek alternative treatments that are perceived as more strength-based, even if not reimbursed by healthcare insurances. 19 The willingness to pay out-of-pocket for these treatments could suggest that the current treatment options do not fully meet the needs of adults with ADHD. 19

Taken together, the scientific literature on the strengths associated with ADHD is still scarce. 20 Moreover, most studies on ADHD have included clinical samples of children, and less research has focused on adults’ experiences with the diagnosis. However, there are a few recent qualitative studies that have explored the positive experiences of adults with ADHD. A review on qualitative research examining the lived experience of adults with ADHD indicates that certain aspects of ADHD can be experienced as positive. 21 Within these studies, attributes like energy, creativity, determination, hyperfocus, adventurousness, curiosity and resilience were emphasised. 22–25 However, these studies have largely included small samples of high-functioning adults with ADHD. One exception is the study by Schippers et al, 26 which applied both qualitative and quantitative methods to examine perceived positive characteristics with ADHD in a large sample of 206 adults with ADHD. 26 Almost all of the participants in the study reported positive aspects related to ADHD, with core themes being creativity, being dynamic, flexibility, socioaffective skills and higher order cognitive skills. There are also a few quantitative studies that have focused on positive aspects of ADHD, in particular, creativity. A review of the link between creativity and ADHD has also shown that creative abilities and achievements were high among individuals with both clinical and subclinical symptoms of ADHD. 20 In line with this, some studies have found ADHD to be associated with entrepreneurial intentions and initiation of entrepreneurial actions. 27 28 As such, these studies highlight that despite the well-known challenges associated with ADHD, there are also several strengths that may be linked to having the diagnosis.

The current study employs a qualitative design to identify and explore positive aspects of having ADHD. By including a fairly large group of adults with ADHD seeking psychological help (n=50), the study further aims to shed light on how these positive aspects of the diagnosis can be used as part of psychological interventions for this group of adults. In this regard, the current study follows up on findings from previous qualitative studies that explored positive experiences with having the diagnosis. It also resonates with studies, indicating that adults with ADHD advocate for treatment options that are less deficit-oriented. We, thus, believe that an investigation into the positive experiences of help-seeking adults with ADHD would contribute to fill an important gap in the research field. A two-folded focus on both the strengths and challenges related to ADHD may further have a countereffect on the public stigmatisation associated with the diagnosis and help to empower individuals with the diagnosis.

Study design

The current study is a qualitative investigation including written responses from adults with ADHD to an open-ended question about self-perceived positive aspects of having ADHD. The empirical material was analysed using thematic analysis with hermeneutic phenomenological framework. 29 30

Study context

The data used in the current study originate from a larger clinical trial of a self-guided internet-delivered intervention for adults with ADHD. 31 The clinical trial was a multiple randomised controlled trial, including 109 adults with ADHD aiming to examine whether SMS reminders would improve treatment adherence. The self-guided intervention was accessed online and included seven modules targeting common themes and challenges related to ADHD. The first module was an introduction module, whereas the second to sixth module focused on inattention, inhibitory control, emotion dysregulation, planning and organisation and self-acceptance, and included instructions to various coping strategies. The seventh and last module was a summary module of the entire programme (see Kenter et al 32 for a more detailed description of the intervention). The majority of the participants who responded to the postassessment reported to be satisfied with the intervention. The participants received a gift card of 400 NOK (38 EUR) for their participation in the clinical trial, regardless of whether they answered the question assessing positive aspects of ADHD.

The original study protocol planned to use both qualitative and quantitative methods for data analysis, but it was not planned to examine positive aspects of ADHD. However, when reviewing the data, we were struck by its richness and the number of answers given to this open and non-obligatory question on positive aspects of ADHD, which inspired us to conduct a more in-depth examination of the empirical material.

Recruitment and inclusion criteria

Participants who were eligible to participate in the clinical trial were adults with ADHD living in Norway. The participants were recruited through the Norwegian ADHD patient association, via the associations’ Facebook page and email listings. Their members received a link to our project website, where they could read about the study and complete a prescreening survey to confirm their eligibility. The participants who were eligible were invited to a telephone screening interview performed by a clinical psychologist or a psychiatric nurse. In the telephone screening, the participants had to confirm a diagnosis of ADHD, give information about the name of the diagnosing physician, the diagnosing institution and the date of diagnostic decision. They were also asked about current ADHD symptoms, everyday functioning and treatment. Comorbid psychiatric disorders, including depression, suicidality, psychosis, bipolar disorder and substance abuse, were assessed through the Mini-International Neuropsychiatric Interview (MINI) 33 as part of the telephone screening. This was done to ensure that participants in need of other treatment interventions were not included in the trial. Moreover, all participants had to give their national identity number, which was used to confirm their identity and secure safe login to the online intervention portal. Following inclusion, the participants gave their informed consent to participate and completed the preintervention assessment, including the Adult ADHD Self-Report Scale, used to assess core ADHD symptoms.

The inclusion criteria for the clinical trial were: (a) age 18 years or older; (b) a diagnosis of ADHD; (c) access to a computer or smartphone with internet access, (d) the ability to read and write the Norwegian language. The exclusion criteria were: (a) severe mental illness, such as major depression, suicidality, bipolar disorder, psychosis or substance abuse disorder; (b) currently participating in another psychological treatment. All participants who responded to the question assessing positive aspects of ADHD were included in the current study.

Data collection

The data were collected between June and October 2020. The data material consisted of the participants’ written responses to the question: ‘What do you experience as positive aspects of having ADHD?’. Along with the question, the participants were given some additional guiding questions that could help them write their response: (a) ‘is there any positive aspects related to having ADHD? (b) has ADHD given you any useful knowledge or experiences? (c) has ADHD helped you get in contact with someone you appreciate? These guiding questions were included as examples to help the participants remind themselves of experiences of positive aspects related to having ADHD. The module page also gave some examples of positive characteristics that adults with ADHD may experience, based on previous studies, including being creative, accepting of others, fun, active, explorative, spontaneous and open minded. Considering that the question was text based and, therefore, without the opportunity to ask follow-up questions, we found it necessary to include those examples to provide a context for the participants when answering the question. The participant could write their response to the question in an open-text field on the module page. The mean number of words in the participants’ responses was 72.5, ranging from 1 to 261 words. There were no instructions on number of words or formatting and the question was not obligatory to answer to continue with the module or the intervention. The question was included in the sixth module of intervention, which was named ‘acceptance’ and had an overall focus on self-acceptance and self-compassion, that is, accepting what you cannot change and being kind to yourself. The module included psychoeducation, videos, tasks as well as text and audio instructions to acceptance and self-compassion strategies.

Data analysis

Qualitative analysis.

The data were analysed using thematic analysis, employing a hermeneutic phenomenological framework. Thematic analysis is a well-known qualitative method for identifying and analysing themes or patterns across the data. 29 Unlike some other methods for qualitative data analysis, thematic analysis does not have a pre-existing theoretical framework and it can, therefore, be applied within different frameworks. In line with the framework we have chosen, we acknowledge that the analytic work is an interpretative and inherently subjective activity. To ensure credibility, we have carefully followed the guidelines prescribed for thematic analysis. The thematic analysis followed the six phases described by Braun and Clarke 29 : (1) familiarisation with data, (2) generation of codes, (3) search for themes, (4) reviewing themes, (5) finalise and naming of themes and (6) producing the report.

Following these steps, the first author began the analytic work by reading through the data material and taking notes. This included transferring the data material from the online intervention platform to the research server and reading carefully through each of participants’ responses while taking notes on preliminary thoughts and ideas. As a next step, the first author generated codes in line with the analytic focus of the study: ‘what do adults with ADHD experience as positive aspects with the diagnosis’. To safeguard interpretive credibility, the coding was conducted in a ‘low-inference’ manner, where the codes were phrased closely to the participants original accounts. The data material was coded using NVivo software. 34 See figure 1 for an overview of all the codes and their frequency. Following the coding, the first author created a visual map where the codes that shared similarities were grouped together. The creation of the visual map was intended to provide an overview of the codes and to obtain preliminary ideas regarding categories and themes. All authors were then given an overview of the codes. The first author also presented the codes with illustrative examples of excerpts from the data to the second author to discuss how the quotes were interpreted. The first and second author continued with the third step in the analysis, namely searching for themes. The initial search for themes resulted in a thematic structure of ten themes. All authors were given an overview of these ten initial themes to provide their input and feedback. As a fourth step, the thematic structure was reviewed in more detail by the first and second author. In this analysis, it became clear that certain themes shared some commonalities, for instance, the initial themes ‘energy’ and ‘hyperfocus’ shared overlapping features, and where, thus, merged into one theme. A new thematic structure was identified, resulting in four core themes (shown in figure 1 ). These four themes were then finalised and named by the first and second author (step 5) and the final report was produced by all authors (step 6).

Overview of core themes, subthemes and codes. Note. Core themes are shown within the upper boxes, whereas subthemes are shown the boxes beneath. Codes are shown as bullet points and code frequency is shown in parenthesis.

Quantitative analysis

In addition to the thematic analysis, differences in participant characteristics and ADHD severity scores were examined using independent t-tests and χ 2 . SPSS was used for quantitative analyses.

Reflexivity

We have strived to maintain reflexivity throughout the research process by consistently examining our pre-existing understanding and assumptions. During both data analysis and interpretation of results, the authors have actively engaged in self-reflection and peer discussions to identify our own preconceptions. For instance, ESN, being a clinical psychologist, has generally been taught that symptoms of ADHD and other psychiatric diagnoses are inherently negative attributes and have been less exposed to the potential positive aspects related to ADHD during her clinical training. To address potential biases, the authors revisited the raw data after creation of the themes to ensure that the participants’ perspectives were accurately represented and to validate their own interpretations.

Patient and public involvement

The user involvement in the larger project (INTROMAT), from which the data are derived, has been extensive. Throughout the 5-year project period, there have been arranged several user meetings with adults with ADHD to examine their needs and preferences to psychological interventions for ADHD. Adults with ADHD have also been involved in the development of content and videos to the intervention as well as evaluating the intervention. In these user meetings, we did not address the research question or research design for the current study; however, the focus of the current study was informed by previous qualitative studies involving adults with ADHD who have expressed a wish for both research and treatment interventions for ADHD to have a stronger focus on positive aspects related to ADHD. The results from the current study will be published on the project website where study participants can be informed. We will also present the findings at meetings for the ADHD patient association, which contributed to the recruitment of participants to the current study.

Among the 109 participants of the intervention study, 62 participants accessed the module, which included the question on positive aspects of ADHD, and 50 gave their response. The remaining 47 participants did not access the module and, consequently, did not have the opportunity to view and respond to the question. None of these 47 participants accessed the following module either and was thereby considered to be dropouts. When comparing the responders (N=50) to the non-responders (N=59), there were no significant differences in age, medication status, age when diagnosed, gender, education, employment status or ADHD severity scores.

The final sample included 50 participants, with a large majority being women. All but one were diagnosed with ADHD in adulthood, with 4.8 years being the mean time since being diagnosed. A total of 37 (72.5%) participants were full-time employed or students and 30 (58.8%) participants had higher education. When comparing men and women in the final sample, the men were significantly older than the women (see table 1 ).

Participant characteristics, gender differences and ADHD severity scores

*p < .05.

ADHD, attention deficit hyperactivity disorder; ASRS, Adult ADHD Self-Report Scale; n, number of participants.

Thematic analysis

The participants reported that they experienced a variety of benefits and advantages related to having ADHD, where all but two participants reported that they experienced ADHD to have positive aspects. With regards to ADHD medication, there were two participants who specifically reported that medications contributed to their positive experiences with ADHD, with one participant reporting that the positive aspects of ADHD were only experienced when taking medication. In the thematic analysis, the participants’ positive experiences associated with having ADHD were arranged within four core themes: (1) the dual impact of ADHD characteristics, (2) the unconventional mind, (3) the pursuit of new experiences and (4) resilience and growth (See table 2 ).

Overview of core themes

ADHD, attention deficit hyperactivity disorder.

Theme 1: the dual impact of ADHD characteristics

Many of the participants stated that even core characteristics of ADHD, such as hyperactivity and impulsivity, could be experienced as positive features. Although these core characteristics could be troublesome, they could also be advantageous and beneficial in some situations.

High levels of energy and drive were reported to be useful in many contexts, such as during physical labour, sports, social events or home renovation. One participant stated: I am active. I am often able to do a lot in a short amount of time, and then I get to experience more (woman, 30 years). However, although there were positive aspects to having high energy levels, there could also be downsides: I have understood that my energy can be used for a lot of good, and that if I use it wrong, it can make things challenging (woman, 26 years). The participants also reported that they did not tire as easily as others: If it is something I really like, I have better endurance than others. I can work on something I enjoy forever without stopping (woman, 26 years).

Several participants also reported spontaneity and risk taking, which may be categorised as impulsive traits, as positive aspects associated with ADHD: I am spontaneous/impulsive. I can easily just ‘jump into it’ and that has given me a lot of great experiences (woman, 30 years). It was also mentioned that spontaneity contributed to memorable experiences and learning. However, some emphasised that spontaneity could be challenging as well: I am not really that fond of that spontaneous side of myself because I experience losing control, but at the same time it has given me unique friendships, relations and possibilities (woman, 28 years).

Hyperfocusing, the ability to have an intense focus on an activity for a longer period of time, was commonly mentioned as an advantage of having ADHD. The participants stated that if they were really interested in a topic, they could maintain focus for a long time without being distracted. Hyperfocus was mentioned to be a contributing factor for completing demanding educational courses, school exams and job assignments. One participant stated that hyperfocusing served as a compensatory strategy: I think my ADHD has helped me throughout the exam periods. If it had not been for a kind of hyperfocus, it would not have worked. But then again, I might not have postponed the reading for so long if I did not have ADHD (woman, 23 years). Another participant emphasised that the hyperfocus on useful task for it to be considered as a positive aspect of ADHD: The only positive is hyperfocus on tasks that are really exciting, but for ADHD to be considered positive in this setting, the task has to be something useful, such as school or work (man, 31 years). Most of the participants did not report inattention be positive, however, one participant explicitly mentioned inattention to also have upsides: Inattention can be nice when I actually need to change focus, if something happens while I am driving etc. It is also nice because I have observed some amusing conversations and such when I am actually supposed to be doing something else (woman, 26 years).

Theme 2: the unconventional mind

Many participants reported that they experienced unconventional thinking and behaviour as positive aspects of having ADHD. This included characteristics such as being creative, having novel ideas, seeing things from a different perspective than others and being good at finding solutions. At the same time, it was also emphasised that the social context and expectations present in one’s sociocultural environment could sometimes be an obstacle for utilising these strengths.

Creativity was emphasised to be a positive aspect of ADHD by many participants: Creativity and being able to think outside the norm is something I really appreciate (woman, 26 years).

Creativity was reported to help one to start new projects and find good solutions at work as well as make everyday life more exciting: I am creative and solution-oriented and very passionate about the things that I am interested in (man, 32 years). Creativity was also mentioned to be a good quality when it came to parenting as it facilitated playfulness with one’s children. Although creativity was viewed as a positive trait by many participants, some emphasised a complexity: From my experience in a work-related context, thinking outside the box is not as accepted in all contexts, despite good results’ (woman, 27 years). With this, the participant underscores that whether a quality is deemed as ‘good’ or ‘bad’ is also dependent on one’s social context.

There were also participants who described that they could be socially unconventional and go outside the norm: I do not care that much about what other think (woman, 41 years). Some reported that they could be quite straightforward, unafraid and uninhibited in social situations: I am pretty forward, and I am not afraid to take up space when I need a bit of attention. I know a lot of people and that is probably because I am not scared to say hi to new people (woman, 23 years).

Theme 3: the pursuit of new experiences

There were several participants who reported that they experienced adventurousness and novelty-seeking as positive aspects of ADHD. Being explorative also appeared to be connected to being both curious and courageous, with some participants describing that they were curious of the unknown and not afraid to embark on new ventures.

Many emphasised that they were curious and enjoyed trying new things and seeking new experiences. The participants also reported that they enjoyed learning new things: I seek new environments where I can learn new things (woman, 29 years). Because they enjoyed learning, they also acquired knowledge about various topics. In line with this, one participant also underlined that they would not give up easily when attempting to learn something new: I enjoy trying new things, and if I do not get it right the first time, I will examine the possibility of trying a simpler method. (woman, 30 years). To enjoy novelty was also reported to be of significance to one’s choice of occupation: I enjoy trying new things and changes. This is the reason why I have the job that I have (woman, 42 years).

There were also reports about being courageous and unafraid, which could push one to seek new experiences: I have experienced things that only would have happened by taking a risk (man, 62 years). Moreover, being impulsive could make one more daring: I dare more than when I sit down and think about it (woman, 29 years).

Theme 4: resilience and growth

The final theme centres around the participants’ experiences of growth and insight after facing adversity. The participants underscored that although ADHD indeed could be challenging, especially the process towards being diagnosed with ADHD, coping with these challenges could also foster resilience and growth.

Some of the participants reported to have a better understanding and acceptance for themselves because of ADHD: Being diagnosed with ADHD made me learn a lot about myself. Things I perhaps have been annoyed about, I can now accept and think that it is not ‘my fault’ in a way (woman, 30 years). Although the process of getting diagnosed could be tough, it could also give valuable insight: The road to my final ADHD diagnosis has been so long and cruel, but I would not have been without all the pain and unbearable years, and all that experience made me know myself in a completely unique way, and I have gotten a very valued quality when it comes to being able to reflect over situations both I and others are in (woman, 25 years).

The participants also expressed resilience after coping with previous challenges: ’ am better at handling resistance or challenges now, because I have learned to handle such challenges, it is part of life to have ups and down s (woman, 51 years). Likewise, coping with challenges could also make one more persistent: I have learned to not give up in the face of resistance. Maybe I must take some detours, do things differently than others, find out what works for me and trust myself, but the point is, I can make it if I want to (woman, 28 years).

The experience of receiving the diagnosis appeared to be especially important: To get the diagnosis was a relief because it gave me an explanation for why I did things I did not understand earlier, such as why I was not able to shut up, but talk without thinking, and why my emotions fluctuate so much, and often without me understanding why. It has given me more understanding and acceptance for myself (woman, 57 years). When the participants learned about the diagnosis, it allowed them to be more kind towards themselves: I discovered that I have ADHD in adulthood, so I lived most of my life in the belief that I am like everyone else. I have had high expectations to myself, compared myself to others, and achieved a lot (…) So when I found out about my challenges, it all became like a piece of cake. I could with good reasons lower the expectations to myself and finally rest with a clear conscience (woman, 37 years).

The participants also reported that they were non-judgemental and accepting of other people: Since I am such a “fool” I don’t judge others for being it (woman, 24 years). The participants also reported to be more empathic and understanding of others’ point of view. Several participants had jobs that involved working with people with disabilities, where having ADHD themselves could help them to connect with their students or patients: As a teacher, ADHD helps me to understand students that have a learning disability (man, 31 years). Another participant stated: I understand a part of the youth on a different level than my colleagu|es, and I therefore experience that I am able to get a better connection with the students others find it difficult to get close to (woman, 44 years). Another participant also shared similar experiences: I notice that I can meet children with ADHD with more understanding, so they feel safe with me quickly, and I know I can help them in challenging situations, or prepare them a bit extra, so that they are able to get through their school day (woman, 30 years).

The current study aimed to identify and explore positive aspects of having ADHD from the perspective of help-seeking adults with the diagnosis. The participants’ accounts of positive experiences of having ADHD could be arranged within the following four themes: (1) the dual impact of ADHD characteristics, (2) the unconventional mind, (3) the pursuit of new experiences and (4) resilience and growth. Through the discussion, we further seek to highlight how positive experiences with the diagnosis can be used in treatment interventions.

The characteristics of ADHD could be experienced as a double-edged sword, where the traits could be seen as both challenging and beneficial. This is in accordance with findings in several studies included in the review by Ginapp et al 21 and Schippers et al 26 . The direction of this relationship further seemed to be dependent on context and the norms in one’s sociocultural environment, where certain qualities could be deemed as beneficial in some situations, but undesirable in other situations. Although one’s environment appeared to be central in the participants’ experience of duality related to ADHD characteristics, individual factors are still important to take into consideration; one person might find a certain characteristic as beneficial, while another might not share the same perspective. As such, whether a characteristic is seen as positive or negative is likely dependent on a variety of factors, including individual factors, environmental factors and the interaction between the two.

From the perspective of the participants, it appeared that even core diagnostic characteristics of ADHD could be experienced as advantageous. For instance, the high energy associated with hyperactivity could be considered as an advantage in certain social settings and within sports, whereas hyperfocus could be beneficial during school exams or at work. These findings are in line with results reported in previous qualitative studies. 22 23 As such, the analytic findings support the notion that some of the characteristics associated with ADHD can be reframed in a more positive manner. Given the high persistence of ADHD symptoms into adulthood, helping adults to explore potential advantages of their symptoms in a treatment setting could perhaps have favourable outcomes for treatment and improve life satisfaction 17

On the other hand, the present findings resonate with results from several other previous studies showing that ADHD characteristics are associated with problems that affect daily-life functioning. 35–37 Based on the current reports, it appeared that the participants had to figure out the ways to make ADHD work for them, with certain traits, such as hyperfocus and high energy, only being considered beneficial under the right circumstances. This reasoning can imply that a key step in psychological interventions for ADHD would be to not only identify the participants’ strengths but also to examine in what contexts these strengths are useful and potential pitfalls or obstacles for utilising them.

Interestingly, only one of the participants reported inattention to be beneficial. In relation to this, a study on ADHD and identity among youth with ADHD found that while several participants experienced positive sides to hyperactivity and impulsivity and integrated these as part of their identity, inattentive symptoms were not associated with such positive experiences. 38 The experience of living with inattentive versus hyperactive/impulsive traits should be an interesting topic for future studies.

The findings further show that creativity seems to be experienced as a core positive aspect of having ADHD. These findings are in line with results from previous studies, which have associated ADHD symptoms with certain qualities of creativity. 39 40 Distractibility has, for example, been associated with creative achievements. 41 As such, it may be that distractibility makes one notice more so-called ‘irrelevant’ information in one’s environment, which later may be helpful in generating more original ideas. 41 Given that creativity may be a strength of ADHD, it should be possible to take advantage of this quality in treatment settings, for example, by including more creative tasks in psychosocial interventions to facilitate engagement and adherence.

The current accounts further show that traits such as adventurousness, exploration and courage may be seen as strengths of ADHD. Such strengths have also been reported by adults with ADHD in previous studies. 23 25 42 In their study, Newark et al 42 found courage to be a resource among adults with ADHD and they further linked this trait to self-efficacy and self-esteem. The authors further emphasised that courage could be a valuable skill in therapy, a situation where clients indeed are faced with both challenges and novel experiences. 36

Lastly, it appeared like coping with the challenges associated with ADHD could lead to resilience and growth for some participants. There were reports about understanding oneself and others in a more nuanced manner, and successful coping was seen as making them more fit to cope with future challenges. This is in line with findings from a previous study including women with ADHD, where the participants identified positive learning from the challenges they had faced. 25 When people are faced with adversity, they often underestimate their abilities to cope with the emotional distress and overestimate the intensity and impact of the particular event. 43 In line with this, some participants seemed to cope with the challenges related to ADHD in a resilient manner and perhaps even experience growth during times of adversity. These findings may be understood within Dombrowski’s theory of positive disintegration, which posits that emotional difficulty and turmoil are necessary for human growth and development. 44 It has further been suggested that resilience is linked to impulsivity, where impulsive traits may help adults to faster move on from their problems, which may be useful in therapy settings. 45

Implications

The clinical implications of the findings may be to incorporate a stronger focus on strengths and resources in both the assessment and treatment of ADHD in adulthood. There is a consensus within psychotherapy that treatment should not only focus on the absence of symptoms but also on recovery, coping, well-being and growth. 46 However, adults with ADHD still report current treatment options to be too deficit oriented. 19 By putting an emphasis on the full range of experiences related to ADHD, both good and bad, one might be able to offer treatment interventions more in line with the needs of adults with ADHD, which may be favourable for treatment engagement and clinical outcomes. For instance, therapist could help adults with ADHD to identify strengths, which may be beneficial for self-esteem and self-efficacy. Within cognitive-behavioural therapy, one could also use positive experiences with ADHD to reframe negative automatic thoughts or maladaptive cognitions. These speculations should indeed provide interesting topics for further studies. A focus on positive sides to ADHD within research may also have societal implications by changing social perception around ADHD and by this reducing stigma related to the diagnosis.

Strengths and limitations

The current study is one of few studies that focus on positive aspects of ADHD, and one of few with a fairly large sample size. Moreover, this study is the only study investigating positive aspects in a sample of help-seeking adults with ADHD. Still, several limitations should be noted. With the current study design and analysis, we can only explore the participants’ experiences with positive aspects of ADHD and give a thematic structure of these experiences. However, future studies combining qualitative and quantitative analyses are needed to further evaluate the generalisability of the positive aspects of ADHD reported in this and previous studies. The sample of the present study was restricted to only include participants who responded to a question regarding positive traits, which only was about half of the participants in the clinical trial. The lower response rate is likely due to the question being asked at the end of the intervention and several participants had been lost to drop-out and thus never accessed the question on positive aspects of ADHD. The impact of being participants in a psychological intervention should also be commented on. For example, it is possible that taking part in the intervention increased the participants’ positive beliefs about themselves and ADHD. The participants did also receive some examples of positive aspects with ADHD in the intervention, which may have influenced their answers. We found it necessary to include these examples since the data collection was conducted online without the guidance of a researcher. As such, when conducting the data analysis and creating the themes, we were careful to examine depth and richness in the participants’ answers and not only frequency. The examples were reported 47 times in the data material, with creativity being the trait most frequently referred to in the data material (27 references). However, creativity was also the most frequently mentioned positive aspect of ADHD in Schippers et al ’ s study. Moreover, we found it reassuring that the participants’ answers went beyond the examples given. The participants of the study mainly consisted of high-functioning women in their 20s and 30s who were diagnosed with ADHD as adults and were seeking psychological help for ADHD. The sample did therefore include more females than males, which makes the findings less transferable to males since the clinical expression of ADHD is known to vary with gender. 47 It may also be seen as a limitation that the ADHD diagnosis was based on self-report. To ensure validity of the diagnosis, it could have been beneficial to conduct a clinical re-examination to confirm that the participants met the diagnostic criteria. However, all participants were asked to report the date, venue and diagnosing healthcare professional for the diagnosis as well as their national identity number. We, therefore, have trust in the participants’ reports. In addition, because participants with ongoing severe mental illness were excluded from the study, the participants are most likely individuals within the less severe end of the ADHD symptom spectrum.

Future directions

The findings from this study need to be validated by future studies. These studies should not only investigate characteristics of strengths related to ADHD but also in what contexts these strengths are useful and beneficial. Moreover, future studies should investigate the impact of strength-based treatments on both treatment engagement and clinical outcomes. Future research should aim for the development of a valid procedure to assess strengths and positive qualities of adults with ADHD. Moreover, it would also be interesting for future studies to include other observers’ impressions of positive aspects related to ADHD, for instance, family members and clinicians.

The aim of the current study was to identify and explore positive aspects of having ADHD from the perspective of help-seeking adults with the diagnosis. From the perspective of the participants, the characteristics of ADHD could be both beneficial and challenging, depending on the individuals’ contextual environment. For clinicians, it may be important to examine the individual’s positive experiences of ADHD, as this should be capitalised on within treatment. A stronger focus on positive aspects of ADHD in treatment interventions, alongside the challenges, may also help to contribute to support a more ability-oriented view of ADHD.

Supplementary Material

Contributors: ESN contributed to the recruitment of participants for the clinical trial, was responsible for the thematic analysis, interpretation of the results, and drafting of the manuscript. FG contributed substantially to the thematic analysis and interpretation of the results, and with comments and input to different versions of the manuscript. TN was responsible for the clinical trial (PI) and contributed with comments and input to the manuscript. AL was responsible for the idea of the current study and contributed substantially with comments and input on different drafts of the manuscript. All authors have read and accepted the final draft of the manuscript. ESN acts as the guarantor for the work.

Funding: The data used in the current study are from the INTROMAT project, funded by The Research Council of Norway (grant: 259293). ESN received funding from the Western Norway Regional Health Authorities (Helse Vest) for her doctoral thesis (grant: F-11016).

Competing interests: None declared.

Patient and public involvement: Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by Regional Committees for Medical and Health Research, Region West Reference ID: 90483. Participants gave informed consent to participate in the study before taking part.

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Patient experiences: a qualitative systematic review of chemotherapy adherence

• Amineh Rashidi 1 ,
• Susma Thapa 1 ,
• Wasana Sandamali Kahawaththa Palliya Guruge 1 &
• Shubhpreet Kaur 1  

BMC Cancer volume  24 , Article number:  658 ( 2024 ) Cite this article

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Adherence to chemotherapy treatment is recognized as a crucial health concern, especially in managing cancer patients. Chemotherapy presents challenges for patients, as it can lead to potential side effects that may adversely affect their mobility and overall function. Patients may sometimes neglect to communicate these side effects to health professionals, which can impact treatment management and leave their unresolved needs unaddressed. However, there is limited understanding of how patients’ experiences contribute to improving adherence to chemotherapy treatment and the provision of appropriate support. Therefore, gaining insights into patients’ experiences is crucial for enhancing the accompaniment and support provided during chemotherapy.

This review synthesizes qualitative literature on chemotherapy adherence within the context of patients’ experiences. Data were collected from Medline, Web of Science, CINAHL, PsychINFO, Embase, Scopus, and the Cochrane Library, systematically searched from 2006 to 2023. Keywords and MeSH terms were utilized to identify relevant research published in English. Thirteen articles were included in this review. Five key themes were synthesized from the findings, including positive outlook, receiving support, side effects, concerns about efficacy, and unmet information needs. The review underscores the importance for healthcare providers, particularly nurses, to focus on providing comprehensive information about chemotherapy treatment to patients. Adopting recommended strategies may assist patients in clinical practice settings in enhancing adherence to chemotherapy treatment and improving health outcomes for individuals living with cancer.

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Introduction

Cancer can affect anyone and is recognized as a chronic disease characterized by abnormal cell multiplication in the body [ 1 ]. While cancer is prevalent worldwide, approximately 70% of cancer-related deaths occur in low- to middle-income nations [ 1 ]. Disparities in cancer outcomes are primarily attributed to variations in the accessibility of comprehensive diagnosis and treatment among countries [ 1 , 2 ]. Cancer treatment comes in various forms; however, chemotherapy is the most widely used approach [ 3 ]. Patients undergoing chemotherapy experience both disease-related and treatment-related adverse effects, significantly impacting their quality of life [ 4 ]. Despite these challenges, many cancer patients adhere to treatment in the hope of survival [ 5 ]. However, some studies have shown that concerns about treatment efficacy may hinder treatment adherence [ 6 ]. Adherence is defined as “the extent to which a person’s behaviour aligns with the recommendations of healthcare providers“ [ 7 ]. Additionally, treatment adherence is influenced by the information provided by healthcare professionals following a cancer diagnosis [ 8 ]. Patient experiences suggest that the decision to adhere to treatment is often influenced by personal factors, with family support playing a crucial role [ 8 ]. Furthermore, providing adequate information about chemotherapy, including its benefits and consequences, can help individuals living with cancer gain a better understanding of the advantages associated with adhering to chemotherapy treatment [ 9 ].

Recognizing the importance of adhering to chemotherapy treatment and understanding the impact of individual experiences of chemotherapy adherence would aid in identifying determinants of adherence and non-adherence that are modifiable through effective interventions [ 10 ]. Recently, systematic reviews have focused on experiences and adherence in breast cancer [ 11 ], self-management of chemotherapy in cancer patients [ 12 ], and the influence of medication side effects on adherence [ 13 ]. However, these reviews were narrow in scope, and to date, no review has integrated the findings of qualitative studies designed to explore both positive and negative experiences regarding chemotherapy treatment adherence. This review aims to synthesize the qualitative literature on chemotherapy adherence within the context of patients’ experiences.

This review was conducted in accordance with the Joanna Briggs Institute [ 14 ] guidelines for systemic review involving meta-aggregation. This review was registered in PROSPERO (CRD42021270459).

Search methods

The searches for peer reviewed publications in English from January 2006-September 2023 were conducted by using keywords, medical subject headings (MeSH) terms and Boolean operators ‘AND’ and ‘OR’, which are presented in the table in Appendix 1 . The searches were performed in a systematic manner in core databases such including Embase, Medline, PsycINFO, CINAHL, Web of Science, Cochrane Library, Scopus and the Joanna Briggs Institute (JBI). The search strategy was developed from keywords and medical subject headings (MeSH) terms. Librarian’s support and advice were sought in forming of the search strategies.

Study selection and inclusion criteria

The systematic search was conducted on each database and all articles were exported to Endnote and duplicates records were removed. Then, title and abstract of the full text was screened by two independent reviewers against the inclusion criteria. For this review, populations were patients aged 18 and over with cancer, the phenomenon of interest was experiences on chemotherapy adherence and context was considered as hospitals, communities, rehabilitation centres, outpatient clinics, and residential aged care. All peer-reviewed qualitative study design were also considered for inclusion. Studies included in this review were classified as primary research, published in English since 2006, some intervention implemented to improve adherence to treatment. This review excluded any studies that related to with cancer and mental health condition, animal studies and grey literature.

Quality appraisal and data extraction

The JBI Qualitative Assessment and Review Instrument for qualitative studies was used to assess the methodological quality of the included studies, which was conducted by the primary and second reviewers independently. There was no disagreement between the reviews. The qualitative data on objectives, study population, context, study methods, and the phenomena of interest and findings form the included studies were extracted.

Data synthesis

The meta-aggregation approach was used to combine the results with similar meaning. The primary and secondary reviewers created categories based on the meanings and concept. These categories were supported by direct quotations from participants. The findings were assess based on three levels of evidence, including unequivocal, credible, and unsupported [ 15 , 16 ]. Findings with no quotation were not considered for synthesis in this review. The categories and findings were also discussed by the third and fourth reviewers until a consensus was reached. The review was approved by the Edith Cowan University Human Research Ethics Committee (2021–02896).

Study inclusion

A total of 4145 records were identified through a systematic search. Duplicates ( n  = 647) were excluded. Two independent reviewers conducted screening process. The remaining articles ( n  = 3498) were examined for title and abstract screening. Then, the full text screening conducted, yielded 13 articles to be included in the final synthesis see Appendix 2 .

Methodological quality of included studies

All included qualitative studies scored between 7 and 9, which is displayed in Appendix 3 . The congruity between the research methodology and the research question or objectives, followed by applying appropriate data collection and data analysis were observed in all included studies. Only one study [ 17 ] indicated the researcher’s statement regarding cultural or theoretical perspectives. Three studies [ 18 , 19 , 20 ] identified the influence of the researcher on the research and vice-versa.

Characteristics of included studies

Most of studies conducted semi-structured and in-depth interviews, one study used narrative stories [ 19 ], one study used focus group discussion [ 21 ], and one study combined focus group and interview [ 22 ] to collect data. All studies conducted outpatient’s clinic, community, or hospital settings [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. The study characteristics presented in Appendix 4 .

Review findings

Eighteen findings were extracted and synthesised into five categories: positive outlook, support, side effects, concern about efficacy and unmet information needs.

Positive outlook

Five studies discussed the link between positivity and hope and chemotherapy adherence [ 19 , 20 , 23 , 27 , 28 ]. Five studies commented that feeling positive and avoid the negativity and worry could encourage people to adhere in their mindset chemotherapy: “ I think the main thing for me was just keeping a positive attitude and not worrying, not letting myself worry about it ” [ 20 ]. Participants also considered the positive thoughts as a coping mechanism, that would help them to adhere and complete chemotherapy: “ I’m just real positive on how everything is going. I’m confident in the chemo, and I’m hoping to get out of her soon ” [ 23 ]. Viewing chemotherapy as part of their treatment regimen and having awareness of negative consequences of non-adherence to chemotherapy encouraged them to adhere chemotherapy: “ If I do not take medicine, I do not think I will be able to live ” [ 28 ]. Adhering chemotherapy was described as a survivor tool which helped people to control cancer-related symptoms: “ it is what is going to restore me. If it wasn’t this treatment, maybe I wasn’t here talking to you. So, I have to focus in what he is going to give me, life !” [ 27 ]. Similarly, people accepted the medical facts and prevent their life from worsening; “ without the treatment, it goes the wrong way. It is hard, but I have accepted it from the beginning, yes. This is how it is. I cannot do anything about it. Just have to accept it ” [ 19 ].

Finding from six studies contributed to this category [ 20 , 21 , 23 , 24 , 25 , 29 ]. Providing support from families and friends most important to the people. Receiving support from family members enhanced a sense responsibility towards their families, as they believed to survive for their family even if suffered: “ yes, I just thought that if something comes back again and I say no, then I have to look my family and friends in the eye and say I could have prevented it, perhaps. Now, if something comes back again, I can say I did everything I could. Cancer is bad enough without someone saying: It’s your own fault!!” [ 29 ]. Also, emotional support from family was described as important in helping and meeting their needs, and through facilitation helped people to adhere chemotherapy: “ people who genuinely mean the support that they’re giving […] just the pure joy on my daughter’s face for helping me. she was there day and night for me if I needed it, and that I think is the main thing not to have someone begrudgingly looking after you ” [ 20 ]. Another study discussed the role family, friends and social media as the best source of support during their treatment to adhere and continue “ I have tons of friends on Facebook, believe it or not, and it’s amazing how many people are supportive in that way, you know, just sending get-well wishes. I can’t imagine going through this like 10 years ago whenever stuff like that wasn’t around ” [ 23 ]. Receiving support from social workers was particularly helpful during chemotherapy in encouraging adherence to the chemotherapy: “ the social worker told me that love is courage. That was a huge encouragement, and I began to encourage myself ” [ 25 ].

Side effects

Findings from five studies informed this category [ 17 , 21 , 22 , 25 , 26 ]. Physical side effects were described by some as the most unpleasure experience: “ the side effects were very uncomfortable. I felt pain, fatigue, nausea, and dizziness that limited my daily activities. Sometimes, I was thinking about not keeping to my chemotherapy schedule due to those side effect ” [ 17 ]. The impact of side effects affected peoples’ ability to maintain their independence and self-care: “ I couldn’t walk because I didn’t have the energy, but I wouldn’t have dared to go out because the diarrhoea was so bad. Sometimes I couldn’t even get to the toilet; that’s very embarrassing because you feel like you’re a baby ” [ 26 ]. Some perceived that this resulted in being unable to perform independently: “ I was incredibly weak and then you still have to do things and you can’t manage it ” [ 22 ]. These side effect also decreased their quality of life “ I felt nauseated whenever I smelled food. I simply had no appetite when food was placed in front of me. I lost my sense of taste. Food had no taste anymore ” [ 25 ]. Although, the side effects impacted on patients´ leisure and free-time activities, they continued to undertake treatment: “ I had to give up doing the things I liked the most, such as going for walks or going to the beach. Routines, daily life in general were affected ” [ 21 ].

Concern about efficacy

Findings form four studies informed this category [ 17 , 18 , 24 , 28 ]. Although being concerned about the efficacy of the chemotherapy and whether or not chemotherapy treatment would be successful, one participant who undertook treatment described: “the efficacy is not so great. It is said to expect about 10% improvement, but I assume that it declines over time ” [ 28 ]. People were worried that such treatment could not cure their cancer and that their body suffered more due to the disease: “ I was really worried about my treatment effectiveness, and I will die shortly ” [ 17 ]. There were doubts expressed about remaining the cancer in the body after chemotherapy: “ there’s always sort of hidden worries in there that whilst they’re not actually taking the tumour away, then you’re wondering whether it’s getting bigger or what’s happening to it, whether it’s spreading or whatever, you know ” [ 24 ]. Uncertainty around the outcome of such treatment, or whether recovering from cancer or not was described as: “it makes you feel confused. You don’t know whether you are going to get better or else whether the illness is going to drag along further” [ 18 ].

Unmet information needs

Five studies contributed to this category [ 17 , 21 , 22 , 23 , 26 ]. The need for adequate information to assimilate information and provide more clarity when discussing complex information were described. Providing information from clinicians was described as minimal: “they explain everything to you and show you the statistics, then you’re supposed to take it all on-board. You could probably go a little bit slower with the different kinds of chemo and grappling with these statistics” [ 26 ]. People also used the internet search to gain information about their cancer or treatments, “I’ve done it (consult google), but I stopped right away because there’s so much information and you don’t know whether it’s true or not ” [ 21 ]. The need to receive from their clinicians to obtain clearer information was described as” I look a lot of stuff up online because it is not explained to me by the team here at the hospital ” [ 23 ]. Feeling overwhelmed with the volume of information could inhibit people to gain a better understanding of chemotherapy treatment and its relevant information: “ you don’t absorb everything that’s being said and an awful lot of information is given to you ” [ 22 ]. People stated that the need to know more information about their cancer, as they were never dared to ask from their clinicians: “ I am a low educated person and come from a rural area; I just follow the doctor’s advice for my health, and I do not dare to ask anything” [ 17 ].

The purpose of this review was to explore patient’s experiences about the chemotherapy adherence. After finalizing the searches, thirteen papers were included in this review that met the inclusion criteria.

The findings of the present review suggest that social support is a crucial element in people’s positive experiences of adhering to chemotherapy. Such support can lead to positive outcomes by providing consistent and timely assistance from family members or healthcare professionals, who play vital roles in maintaining chemotherapy adherence [ 30 ]. Consistent with our study, previous research has highlighted the significant role of family members in offering emotional and physical support, which helps individuals cope better with chemotherapy treatment [ 31 , 32 ]. However, while receiving support from family members reinforces individuals’ sense of responsibility in managing their treatment and their family, it also instils a desire to survive cancer and undergo chemotherapy. One study found that assuming self-responsibility empowers patients undergoing chemotherapy, as they feel a sense of control over their therapy and are less dependent on family members or healthcare professionals [ 33 ]. A qualitative systematic review reported that support from family members enables patients to become more proactive and effective in adhering to their treatment plan [ 34 ]. This review highlights the importance of maintaining a positive outlook and rational beliefs as essential components of chemotherapy adherence. Positive thinking helps individuals recognize their role in chemotherapy treatment and cope more effectively with their illness by accepting it as part of their treatment regimen and viewing it as a tool for survival. This finding is supported by previous studies indicating that positivity and positive affirmations play critical roles in helping individuals adapt to their reality and construct attitudes conducive to chemotherapy adherence [ 35 , 36 ]. Similarly, maintaining a positive mindset can foster more favourable thoughts regarding chemotherapy adherence, ultimately enhancing adherence and overall well-being [ 37 ].

This review identified side effects as a significant negative aspect of the chemotherapy experience, with individuals expressing concerns about how these side effects affected their ability to perform personal self-care tasks and maintain independent living in their daily lives. Previous studies have shown that participants with a history of chemotherapy drug side effects were less likely to adhere to their treatment regimen due to worsening symptoms, which increased the burden of medication side effects [ 38 , 39 ]. For instance, cancer patients who experienced minimal side effects from chemotherapy were at least 3.5 times more likely to adhere to their treatment plan compared to those who experienced side effects [ 40 ]. Despite experiencing side effects, patients were generally willing to accept and adhere to their treatment program, although one study in this review indicated that side effects made some patients unable to maintain treatment adherence. Side effects also decreased quality of life and imposed restrictions on lifestyle, as seen in another study where adverse effects limited individuals in fulfilling daily commitments and returning to normal levels of functioning [ 41 ]. Additionally, unmet needs regarding information on patients’ needs and expectations were common. Healthcare professionals were considered the most important source of information, followed by consultation with the internet. Providing information from healthcare professionals, particularly nurses, can support patients effectively and reinforce treatment adherence [ 42 , 43 ]. Chemotherapy patients often preferred to base their decisions on the recommendations of their care providers and required adequate information retention. Related studies have highlighted that unmet needs among cancer patients are known factors associated with chemotherapy adherence, emphasizing the importance of providing precise information and delivering it by healthcare professionals to improve adherence [ 44 , 45 ]. Doubts about the efficacy of chemotherapy treatment, as the disease may remain latent, were considered negative experiences. Despite these doubts, patients continued their treatment, echoing findings from a study where doubts regarding efficacy were identified as a main concern for chemotherapy adherence. Further research is needed to understand how doubts about treatment efficacy can still encourage patients to adhere to chemotherapy treatment.

Strengths and limitation

The strength of this review lies in its comprehensive search strategy across databases to select appropriate articles. Additionally, the use of JBI guidelines provided a comprehensive and rigorous methodological approach in conducting this review. However, the exclusion of non-English studies, quantitative studies, and studies involving adolescents and children may limit the generalizability of the findings. Furthermore, this review focuses solely on chemotherapy treatment and does not encompass other types of cancer treatment.

Conclusion and practical implications

Based on the discussion of the findings, it is evident that maintaining a positive mentality and receiving social support can enhance chemotherapy adherence. Conversely, experiencing treatment side effects, concerns about efficacy, and unmet information needs may lead to lower adherence. These findings present an opportunity for healthcare professionals, particularly nurses, to develop standardized approaches aimed at facilitating chemotherapy treatment adherence, with a focus on providing comprehensive information. By assessing patients’ needs, healthcare professionals can tailor approaches to promote chemotherapy adherence and improve the survival rates of people living with cancer. Raising awareness and providing education about cancer and chemotherapy treatment can enhance patients’ understanding of the disease and its treatment options. Utilizing videos and reading materials in outpatient clinics and pharmacy settings can broaden the reach of educational efforts. Policy makers and healthcare providers can collaborate to develop sustainable patient education models to optimize patient outcomes in the context of cancer care. A deeper understanding of individual processes related to chemotherapy adherence is necessary to plan the implementation of interventions effectively. Further research examining the experiences of both adherent and non-adherent patients is essential to gain a comprehensive understanding of this topic.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. on our submission system as well.

World Health Organization. Cancer 2021 [ https://www.who.int/news-room/fact-sheets/detail/cancer .

Klapheke A, Yap SA, Pan K, Cress RDDHSDCA. Sociodemographic disparities in chemotherapy treatment and impact on survival among patients with metastatic bladder cancer. Urologic Oncology: Seminars Original Investigations. 2018;36(6):19–308.

Article   Google Scholar  

Moth EB, Kiely BE, Naganathan V, Martin A, Blinman P. How do oncologists make decisions about chemotherapy for their older patients with cancer? A survey of Australian oncologists. Support Care Cancer. 2018;26(2):451–60.

Article   CAS   PubMed   Google Scholar  

Khamboon T, Pakanta I. Intervention for symptom cluster management of fatigue, loss of appetite, and anxiety among patients with lung cancer undergoing chemotherapy. Asia-Pacific J Oncol Nurs. 2021;8(3):267–75.

Garcia ACM, Camargos Junior JB, Sarto KK, Silva Marcelo CAd, Paiva EMC, Nogueira DA, Mills J. Quality of life, self-compassion and mindfulness in cancer patients undergoing chemotherapy: a cross-sectional study. Eur J Oncol Nurs. 2021;51:N.PAG-N.PAG.

Horne R, Chapman SCE, Parham R, Freemantle N, Forbes A, Cooper V. Understanding patients’ adherence-related beliefs about Medicines prescribed for long-term conditions: a Meta-Analytic Review of the necessity-concerns Framework. PLoS ONE. 2013;8(12):e80633.

Article   PubMed   PubMed Central   Google Scholar  

WHO. Adherence to long-term therapies: evidence for action. Geneva, Switzerland: World Health Organisation; 2003.

Google Scholar  

Warby A, Dhillon HM, Kao S, Vardy JL. A survey of patient and caregiver experience with malignant pleural mesothelioma. Support Care Cancer. 2019;27(12):4675–86.

Article   PubMed   Google Scholar  

Arunachalam SS, Shetty AP, Panniyadi N, Meena C, Kumari J, Rani B, et al. Study on knowledge of chemotherapy’s adverse effects and their self-care ability to manage - the cancer survivors impact. Clin Epidemiol Global Health. 2021;11:100765.

Article   CAS   Google Scholar  

Nizet P, Touchefeu Y, Pecout S, Cauchin E, Beaudouin E, Mayol S, et al. Exploring the factors influencing adherence to oral anticancer drugs in patients with digestive cancer: a qualitative study. Support Care Cancer. 2022;30(3):2591–604.

Clancy C, Lynch J, Oconnor P, Dowling M. Breast cancer patients’ experiences of adherence and persistence to oral endocrine therapy: a qualitative evidence synthesis. Eur J Oncol Nurs. 2020;44.

Magalhães B, Fernandes C, Lima L, Martinez-Galiano JM, Santos C. Cancer patients’ experiences on self-management of chemotherapy treatment-related symptoms: A systematic review and thematic synthesis. Eur J Oncol Nurs. 2020;49.

Peddie N, Agnew S, Crawford M, Dixon D, MacPherson I, Fleming L. The impact of medication side effects on adherence and persistence to hormone therapy in breast cancer survivors: a qualitative systematic review and thematic synthesis. Breast. 2021;58:147–59.

Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ: Br Med J. 2009;339(7716):332–6.

Joanna Briggs Institute. The Joanna Briggs Institute critical appraisal tools for use in JBI systematic reviews. Checklist for qualitative research. 2017.

Zachary M, Kylie P, Craig L, Edoardo A, Alan P. Establishing confidence in the output of qualitative research synthesis: the ConQual approach. BMC Med Res Methodol [Internet]. 2014;14(1):108.

Iskandarsyah A, de Klerk C, Suardi DR, Soemitro MP, Sadarjoen SS, Passchier J. Psychosocial and cultural reasons for Delay in seeking help and Nonadherence to treatment in Indonesian women with breast Cancer: a qualitative study. Health Psychol. 2014;33(3):214–21.

Chircop D, Scerri J. The lived experience of patients with Non-hodgkin’s lymphoma undergoing chemotherapy. Eur J Oncol Nurs. 2018;35:117–21.

Kvåle K, Synnes O. Living with life-prolonging chemotherapy—control and meaning‐making in the tension between life and death. Eur J Cancer Care. 2018;27(1):1.

Staneva AA, Beesley VL, Niranjan N, Gibson AF, Rowlands I, Webb PM. I wasn’t gonna let it stop me: exploring women’s experiences of getting through chemotherapy for ovarian cancer. Cancer Nurs. 2019;42(2):E31–8.

Talens A, Guilabert M, Lumbreras B, Aznar MT, López-Pintor E. Medication Experience and Adherence to Oral Chemotherapy: A Qualitative Study of Patients’ and Health Professionals’ Perspectives. Int J Environ Res Public Health. 2021;18(8).

Dumas L, Lidington E, Appadu L, Jupp P, Husson O, Banerjee S, et al. Exploring older women’s attitudes to and experience of treatment for advanced ovarian cancer: a qualitative phenomenological study. Cancers. 2021;13(6):1207.

Albrecht TA, Keim-Malpass J, Boyiadzis M, Rosenzweig M. Psychosocial experiences of young adults diagnosed with acute leukemia during hospitalization for induction chemotherapy treatment. J Hospice Palliat Nurs. 2019;21(2):167–73.

Beaver K, Williamson S, Briggs J. Exploring patient experiences of neo-adjuvant chemotherapy for breast cancer. Eur J Oncol Nurs. 2016;20:77–86.

Chou J-F, Lu YY. Intraperitoneal chemotherapy: the lived experiences of Taiwanese patients with ovarian cancer. Clin J Oncol Nurs. 2019;23(6):E100–6.

Farrell C, Heaven C. Understanding the impact of chemotherapy on dignity for older people and their partners. Eur J Oncol Nurs. 2018;36:82–8.

Wakiuchi J, Silva Marcon S, de Oliveira DC, Aparecida Sales C. Rebuilding subjectivity from the experience of cancer and its treatment. Revista Brasileira De Enfermagem. 2019;72(1):125–33.

Yagasaki K, Komatsu H, Takahashi T. Inner conflict in patients receiving oral anticancer agents: a qualitative study. BMJ Open [Internet]. 2015; 5(4).

Gassmann C, Kolbe N, Brenner A. Experiences and coping strategies of oncology patients undergoing oral chemotherapy: first steps of a grounded theory study. Eur J Oncol Nurs. 2016;23:106–14.

Tang GX, Yan PP, Yan CL, Fu B, Zhu SJ, Zhou LQ, et al. Determinants of suicidal ideation in gynecological cancer patients. Psycho-oncology. 2016;25(1):97–103.

Oven Ustaalioglu B, Acar E, Caliskan M. The predictive factors for perceived social support among cancer patients and caregiver burden of their family caregivers in Turkish population. Int J Psychiatry Clin Pract. 2018;22(1):63–9.

Levkovich I, Cohen M, Karkabi K. The experience of fatigue in breast Cancer patients 1–12 Month Post-chemotherapy: a qualitative study. Behav Med. 2019;45(1):7–18.

Simchowitz B, Shiman L, Spencer J, Brouillard D, Gross A, Connor M, Weingart SN. Perceptions and experiences of patients receiving oral chemotherapy. Clin J Oncol Nurs. 2010;14(4):447–53.

Rashidi A, Kaistha P, Whitehead L, Robinson S. Factors that influence adherence to treatment plans amongst people living with cardiovascular disease: a review of published qualitative research studies. Int J Nurs Stud 2020;110(103727).

Aydogan U, Doganer YC, Komurcu S, Ozturk B, Ozet A, Saglam K. Coping attitudes of cancer patients and their caregivers and quality of life of caregivers. Indian J Palliat Care. 2016;22(2):150–6.

Langford DJ, Morgan S, Cooper B, Paul S, Kober K, Wright F, et al. Association of personality profiles with coping and adjustment to cancer among patients undergoing chemotherapy. Psycho-oncology. 2020;29(6):1060–7.

Jamie MJ, Pensak NA, Sporn NJ, MacDonald JJ, Lennes IT, Safren SA et al. Treatment satisfaction and adherence to oral chemotherapy in patients with Cancer. J Oncol Pract. 2017;13(2).

Tsai Y-F, Huang W-C, Cho S-F, Hsiao H-H, Liu Y-C, Lin S-F, et al. Side effects and medication adherence of tyrosine kinase inhibitors for patients with chronic myeloid leukemia in Taiwan. Medicine. 2018;97(26):415.

D S, M P, G R, S H. Importance of medication adherence and factors affecting it. IP Int J Compr Adv Pharmacolog. 2020;3(2):69–77.

Bekalu YE, Wudu MA, Gashu AW. Adherence to Chemotherapy and Associated factors among patients with Cancer in Amhara Region, Northeastern Ethiopia, 2022. A cross-sectional study. Cancer Control. 2023;30.

Hsu H-C, Liou W-S, Chiang A-J, Tsai S-Y, Jeang S-R, Wu S-L, et al. Longitudinal perceptions of the side effects of chemotherapy in patients with gynecological cancer. Support Care Cancer. 2017;25(11):3457–64.

Gow K, Rashidi A, Whithead L. Factors influencing medication adherence among adults living with diabetes and comorbidities: a qualitative systematic review. Curr Diab Rep. 2023:1–7.

Rashidi A, Whitehead L, Kaistha P. Nurses’ perceptions of factors influencing treatment engagement among patients with cardiovascular diseases: a systematic review. BMC Nurs. 2021;20(1):251.

Zebrack BJ, Block R, Hayes-Lattin B, Embry L, Aguilar C, Meeske KA, et al. Psychosocial service use and unmet need among recently diagnosed adolescent and young adult cancer patients. Cancer. 2013;119(1):201–14.

Timmers L, Boons CCLM, van den Verbrugghe M, Van Hecke A, Hugtenburg JG. Supporting adherence to oral anticancer agents: clinical practice and clues to improve care provided by physicians, nurse practitioners, nurses and pharmacists. BMC Cancer. 2017;17(1).

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First author (AR) and second author (ST) conceived the review and the second author oversight for all stages of the review provided by the second author. All authors (AR), (ST), (WG) and (SK) undertook the literature search. Data extraction, screening the included papers and quality appraisal were undertaken by all authors (AR), (ST), (WG) and (SK). First and second authors (AR) and (ST) analysed the data and wrote the first draft of the manuscript and revised the manuscript and all authors (AR), (ST), (WG) and (SK) approved the final version of the manuscript.

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Rashidi, A., Thapa, S., Kahawaththa Palliya Guruge, W. et al. Patient experiences: a qualitative systematic review of chemotherapy adherence. BMC Cancer 24 , 658 (2024). https://doi.org/10.1186/s12885-024-12353-z

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Enhancing AI competence in health management: students’ experiences with ChatGPT as a learning Tool

• Lior Naamati-Schneider 1  

BMC Medical Education volume  24 , Article number:  598 ( 2024 ) Cite this article

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The healthcare industry has had to adapt to significant shifts caused by technological advancements, demographic changes, economic pressures, and political dynamics. These factors are reshaping the complex ecosystem in which healthcare organizations operate and have forced them to modify their operations in response to the rapidly evolving landscape. The increase in automation and the growing importance of digital and virtual environments are the key drivers necessitating this change. In the healthcare sector in particular, processes of change, including the incorporation of artificial intelligent language models like ChatGPT into daily life, necessitate a reevaluation of digital literacy skills.

This study proposes a novel pedagogical framework that integrates problem-based learning with the use of ChatGPT for undergraduate healthcare management students, while qualitatively exploring the students’ experiences with this technology through a thematic analysis of the reflective journals of 65 students.

Through the data analysis, the researcher identified five main categories: (1) Use of Literacy Skills; (2) User Experiences with ChatGPT; (3) ChatGPT Information Credibility; (4) Challenges and Barriers when Working with ChatGPT; (5) Mastering ChatGPT-Prompting Competencies . The findings show that incorporating digital tools, and particularly ChatGPT, in medical education has a positive impact on students’ digital literacy and on AI Literacy skills.

Conclusions

The results underscore the evolving nature of these skills in an AI-integrated educational environment and offer valuable insights into students’ perceptions and experiences. The study contributes to the broader discourse about the need for updated AI literacy skills in medical education from the early stages of education.

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Introduction

In recent years, the healthcare sector has undergone significant shifts in both local and global contexts. These shifts are primarily attributed to demographic, technological, economic, and political factors. These changes have had a profound impact on the healthcare ecosystem, requiring organizations to adapt their operations and strategies to this evolving landscape [ 1 , 2 ]. In response, healthcare organizations have had to modify their behavior to adapt to this ever-changing reality [ 3 ]. Among the factors that have most significantly affected the healthcare system are technological advancements, automation, and the rise of digital and virtual environments. The impact of these factors gained momentum in December 2019, primarily due to the COVID-19 pandemic. Technological advances, particularly the rise of artificial intelligence (AI) and digital tools, have been central to this transformation, with the COVID-19 pandemic accelerating the need for healthcare systems to adapt and innovate [ 3 , 4 , 5 , 6 , 7 , 8 ]. The integration of AI in healthcare, including the deployment of chatbots like ChatGPT that utilize the Generative Pre-trained Transformer (GPT)—a type of large language model (LLM)—underscores a shift toward digital and AI literacy in medical education and practice. [ 9 , 10 ].

The adoption of AI in healthcare, highlighted by the use of systems like ChatGPT, marks a pivotal shift towards greater digital and AI literacy in medical education and practice [ 9 , 10 , 11 , 12 ]. This reflects the healthcare sector’s broader move towards technological innovation, aiming to enhance patient care and revolutionize healthcare professional training. Incorporating AI, such as ChatGPT, into educational frameworks prepares students for the complexities of modern healthcare, demonstrating AI’s potential to transform both healthcare delivery and professional skill development [ 11 , 12 ].

In the rapidly evolving landscape of AI, where technological developments are occurring at an accelerated pace, there is a significant need for comprehensive research to navigate this ever-changing landscape. In particular, research into the impact of AI on healthcare is still limited, highlighting the urgent need for more focused studies on the implications for medical education and the effective training of healthcare professionals in the use of AI technologies [ 13 , 14 ]. The emergence of LLMs, such as GPT, and their applications in educational frameworks, including chatbots like ChatGPT, has increased the urgency of reassessing the skills required, with a particular focus on digital literacy. This reassessment is essential to determine the continued relevance of these skills or whether a fundamental refocusing is required. Such a re-examination is essential to ensure that the healthcare workforce is adequately prepared for the challenges and opportunities presented by the integration of AI into healthcare practice [ 11 ].

Studies [ 15 , 16 , 17 , 18 ] have identified a significant gap in understanding how digital literacy skills—such as accessing, analyzing, evaluating, and creating digital content—play a role in effectively leveraging LLMs like GPT and their applications, including chatbots such as ChatGPT, within educational frameworks. Furthermore, the successful integration of ChatGPT into educational settings may potentially lessen the reliance on traditional digital literacy skills, prompting a reevaluation of their ongoing relevance [ 19 , 20 ]. This gap underscores the need for more research into the critical role that digital literacy skills hold in the efficient use of technologies like ChatGPT for educational aims, as highlighted by recent literature [ 15 , 17 , 18 ]. ChatGPT’s access to accurate medical information could reduce the need for individual data analysis skills [ 21 , 22 ]. Yet, concerns persist among researchers that its content generation might hinder critical thinking development, including source evaluation and idea generation [ 23 , 24 ].

This qualitative study introduces a pedagogical framework that synergizes problem-based learning with the application of ChatGPT among undergraduate healthcare management students. It aims to qualitatively examine their interactions with this technology, focusing on the transition from traditional digital literacy towards a more advanced AI literacy. This evolution in educational focus is poised to revolutionize the requisite competencies for navigating the dynamic healthcare sector of today.

The rationale behind focusing on ChatGPT stems from its notable accessibility, user-friendly design, and versatility as a comprehensive tool in healthcare settings. Its capability to simulate human-like dialogues positions it as a prime resource for educational initiatives, thereby enriching the pedagogical domain of healthcare management and clinical practices. The unrestricted access to ChatGPT, along with its wide-ranging utility in executing diverse healthcare operations, underscores its capacity to significantly contribute to and spearhead innovation within healthcare education and practices. The selection of ChatGPT, attributed to its approachability and adaptability, marks a strategic endeavor to investigate the impact of artificial intelligence amidst the shifting paradigms of healthcare requirements. Yet, despite the widespread integration of ChatGPT in healthcare, research into the long-term effects and the necessary adaptation of skills and methods remains lacking. [ 11 , 12 ].

Literature review

Ai tools in medical settings.

AI involves creating systems that mimic human cognitive functions such as perception, speech recognition, and decision-making through machine learning. It excels in analyzing data, identifying patterns, and making predictions, offering improvements over traditional data processing. AI’s applications span multiple sectors, including healthcare, at various levels from individual to global [ 25 , 26 ]. The integration of AI into healthcare enhances diagnostic, treatment, and patient care, offering advanced decision-making and predictions [ 9 , 10 , 25 , 27 ].AI technologies enhance clinical decision-making, diagnosis, and treatment by analyzing patient data through machine learning for informed decisions, offering 24/7 support via AI chatbots, and enabling remote monitoring with AI-powered devices like wearable sensors [ 9 , 28 ].

AI facilitates remote patient monitoring, minimizing in-person healthcare visits [ 29 ]. It improves service personalization, with AI assistants managing appointments and reminders, and chatbots streamlining insurance claims, easing provider workloads [ 9 ]. AI automates routine administrative tasks, freeing providers to concentrate on patient care. It streamlines operations, cuts bureaucracy, and analyzes data to improve healthcare management and predict service demand, allowing for better resource allocation. AI’s analysis of patient feedback further aids in enhancing service delivery [ 10 ]. AI integration can transform patient-caregiver dynamics, enhancing diagnosis, treatment, and self-management of health conditions [ 30 ]. While AI integration in healthcare promises significant advancements, it presents challenges, including data management issues and the need for specialized skills.

Sallam [ 14 ] highlights ChatGPT’s potential advantages in healthcare, including enhancing clinical workflows, diagnostics, and personalized medicine. However, challenges such as ethical dilemmas, interpretability issues, and content accuracy must be tackled. In healthcare education, although ChatGPT holds promise for customized learning and creating lifelike clinical scenarios, concerns about bias, plagiarism, and content quality persist. Addressing these concerns necessitates preparing healthcare professionals and students through education and training to navigate the complexities of AI. Additionally, extensive research in these domains is essential [ 6 , 9 , 14 , 31 , 32 ].

Teaching with AI and about AI: advancing education in the digital age

To be able to utilize AI tools effectively and integrate them seamlessly into their everyday work, healthcare professionals need early exposure to AI tools in their education to boost their proficiency and confidence, understanding both their potential and limitations [ 9 , 32 , 33 ]. York et al. [ 32 ] explored medical professionals’ attitudes towards AI in radiology, revealing a positive outlook on AI’s healthcare benefits but also highlighting a notable gap in AI knowledge. This emphasizes the need for enhanced AI training in medical education.

According to Sallam [ 14 ], ChatGPT and other models based on lLLMs have significantly improved healthcare education. They customize responses to student inquiries, curate relevant educational material, and tailor content to individual learning styles. For instance, ChatGPT generates personalized quiz questions, suggests resources to fill knowledge gaps, and adjusts explanations to suit diverse learning preferences. Moreover, it simplifies complex medical concepts, employs analogies and examples for clarity, and offers supplementary materials to enhance comprehension.

Breeding et al. [ 11 ] argued that in medical education, ChatGPT should be viewed as a supplementary tool rather than a substitute for traditional sources. While it offers clear and organized information, medical students still perceive evidence-based sources as more comprehensive. Eysenbach [ 33 ] engaged in a series of dialogues with ChatGPT to explore its integration into medical education. ChatGPT demonstrated proficiency in various tasks, such as grading essays, providing feedback, creating virtual patient scenarios, enhancing medical textbooks, summarizing research articles, and explaining key findings. Nevertheless, it also demonstrated a tendency to produce erroneous responses and fabricated data, including references. Such inaccuracies have the potential to generate student misconceptions, spread misinformation, and cause a decline in critical thinking skills [ 33 ]. Han et al. [ 34 ] conducted a comprehensive examination of ChatGPT’s effectiveness as a pedagogical tool in medical education, focusing on the chatbot’s interaction with delineated educational objectives and tasks. Their findings suggest that while ChatGPT is capable of providing elementary data and explanations, it is not impervious to constraints and sometimes provides incorrect or partial information. The study stresses active learning and analytical reasoning in medical education, emphasizing the importance of understanding basic sciences and the need for expert oversight to ensure AI-generated information accuracy [ 34 ].

Das et al. [ 35 ] evaluated ChatGPT’s efficacy in medical education, focusing on microbiology questions at different difficulty levels. They found that ChatGPT could answer basic and complex microbiology queries with roughly 80% accuracy, indicating its potential as an automated educational tool in medicine. The study underscores the importance of ongoing improvements in training language models to enhance their effectiveness for academic use [ 35 , 36 ].AI implementation in healthcare must be carefully managed to maximize benefits and minimize risks [ 11 , 12 , 35 , 36 ]. With the rapid development of digital technologies and AI tools, particularly in healthcare, students need appropriate resources to use these technologies effectively [ 37 ]. Digital literacy is essential in the 21st century, including skills for interacting with digital content [ 16 , 18 ]. Hence, medical literacy skills should start early in the education of healthcare students.

Digital literacy and eHealth literacy skills

Digital literacy skills encompass a collection of essential abilities necessary for using digital technologies effectively in accessing and retrieving information [ 38 ]. These skills are often viewed as foundational digital literacies that are critical for full participation in the digital era [ 39 ]. The European Commission emphasizes the importance of digital literacy for employability and citizenship. They advocate for policies and programs to enhance digital skills across all segments of society. The EU aims for 70% of adults to have basic digital skills by 2025, focusing on analytical, evaluative, and critical thinking abilities crucial for assessing digital information’s quality and credibility [ 40 ]. Individuals need these skills to discern biases and misinformation in various media formats [ 16 , 17 , 41 ] and evaluate the credibility of online sources [ 42 ]. Critical thinking is crucial for distinguishing between accurate information and misinformation [ 43 ], while data literacy is essential for interpreting data and detecting misleading statistics [ 44 ]. These competencies are fundamental for navigating today’s complex digital information landscape.

eHealth literacy, which incorporates the digital skills needed to access and utilize medical information from digital platforms [ 45 ], is gaining recognition as an integral component of overall health literacy. Enhanced online medical literacy is vital for healthcare professionals and administrators [ 46 ] to adapt to changing demands and improve care management within evolving healthcare paradigms [ 47 ]. Additionally, acquisition of digital competencies has been identified as a valuable strategy that healthcare providers and managers can use to manage the psychological effects of heightened workloads and uncertainty, such as the fear, stress, and anxiety emerging from the COVID-19 pandemic [ 48 ]. These skills enable individuals to use AI as both an independent tool and a supplementary aid in decision-making. However, addressing challenges like bias and academic integrity is crucial when integrating AI into medical education [ 32 , 33 , 49 ]. Critical thinking skills are essential for analyzing digital information, identifying inconsistencies, and evaluating arguments. In today’s era of misinformation, users must verify the accuracy of online content and distinguish between reliable sources and hoaxes [ 43 ]. Data literacy skills are also crucial for interpreting data accurately, detecting misleading statistics, and making informed decisions based on credible sources in the digital age [ 44 ].

Research on digital literacy emphasizes the importance of analytical and evaluative skills. Morgan et al. [ 17 ] found that higher education students struggle most with evaluating digital content for bias and quality. They excel in social literacy skills like communication. This highlights the need to prioritize adaptability in digital literacy, integrating industry-relevant experiences into education to ensure students can navigate and critically assess digital information for real-world applications.

Indeed, since the introduction of ChatGPT in 2022, it has been beneficial in various educational contexts. Nevertheless, concerns have been raised about potential inaccuracies and misinformation that may affect student learning and critical thinking [ 20 ]. Moreover, the potential redundancy of certain digital skills as a result of ChatGPT’s capabilities has also sparked discussions on changing educational objectives [ 19 , 21 , 22 ]. The development of ChatGPT may replace some digital skills as it takes over tasks previously expected of students. Researchers [ 21 , 22 ] argue that it is constantly improving its ability to access accurate medical information, providing reliable advice and treatment options from reputable sources. This ability may render the need for individuals to be adept at information retrieval and evaluation redundant. In other words, ChatGPT’s growing proficiency in tasks such as translation, text summarization, and sentiment analysis, and its ability to generate content like movies [ 23 ] may potentially lead to the underdevelopment of critical thinking skills, including the ability to evaluate source quality and reliability, formulate informed judgments, and generate creative and original ideas [ 24 ]. Indeed, the integration of AI into the healthcare sector raises critical questions about the nature and scope of the digital skills required in the future [ 19 , 20 ].

As AI advances, essential digital competencies may need reassessment to keep pace with technology. This requires forward-thinking digital literacy initiatives, particularly in healthcare education and practice. Proactively addressing the potential impact of AI on human interactions with digital healthcare technologies is critical. This will ensure that healthcare professionals and students are skilled in current digital practices, and prepared for the evolving role of AI in the sector. Despite the swift integration of AI tools in healthcare, and applications like ChatGPT, research on their long-term impacts, effects on users, and the necessary adaptation of skills and methodologies in the ever-evolving learning environment remains insufficient [ 11 , 12 , 15 , 17 , 18 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ].

This study aims to address the intersection of AI adoption in healthcare and its implications for medical education, specifically focusing on the skills required by healthcare professionals. With the rapid incorporation of AI, into healthcare settings, there is an urgent need to reassess the digital literacy skills traditionally emphasized in medical education. This reassessment prompts questions about the ongoing relevance of these skills as AI technologies continue to evolve and expand their role in healthcare [ 13 , 15 , 16 , 17 , 18 , 19 , 20 ].

Research questions

Given the context, this study aims to explore the following qualitative research questions:

How does a pedagogical framework integrating problem-based learning with ChatGPT affect healthcare management undergraduates’ digital literacy skills?

What are students’ experiences with the combined use of problem-based learning and ChatGPT in their healthcare management education?

How do students perceive the shift towards AI-relevant skills as a result of engaging with this integrated pedagogical approach?

Methodology

Methodological approach.

The present research adopts the case study methodology, which entails in-depth empirical research of a current phenomenon within its real-world context [ 50 ]. This approach involves collecting data on human activities within a defined time and space setting, thereby facilitating an understanding of the various processes occurring within the research case. In qualitative research, and particularly in case study research, themes are formulated from the participants’ narratives, thus allowing for the development of arguments or generalizations derived deductively from participants’ statements [ 51 ]. By focusing on our research questions and using a methodological framework that emphasizes depth and context, the study aims to shed light on the transformative impact of AI on medical education and the development of the skills required for future healthcare professionals.

The research was conducted and analyzed by the researcher, who has a PhD in Healthcare Management and over 15 years of experience in qualitative analysis. Her expertise ensures a deep understanding of the study’s qualitative data. Throughout the research, she engaged in continuous reflexive practices to evaluate how her subjectivity and context influenced the study. This included reflecting on her assumptions, considering power dynamics with participants, aligning research paradigms and methods, and understanding the research context [ 59 ].

Participants and research population

The study involved 89 third-year undergraduate students enrolled in a Health System Management degree program, specifically participating in a course on Service Quality in the Healthcare System during the 2023 academic year. The researcher, serving as the lecturer for this course, integrated writing reflective journals into the curriculum as part of the learning process. Following the course’s conclusion and after grades were distributed, the researcher asked students, in adherence to ethical guidelines, if they consented to have their reflective journals analyzed for research purposes, as outlined in the data collection section. Only students who completed all components of the intervention plan outlined for the class were considered potential participants in the research population.

From this group, qualitative data was extracted from the reflective journals of 65 students who consented to participate. The demographic breakdown of this participant subset included 80% females, with an average age of 24.26 years (Standard Deviation = 3.80).

Data collection

Throughout the course, participants were required to keep a reflective journal documenting their learning journey, to be submitted at the end of the semester. The aim of writing the journal was to capture their personal perceptions of their learning experience. They were encouraged to articulate various challenges, obstacles, and positive and negative aspects they encountered [ 52 ]. Specifically, they were asked to describe the main challenges they faced and the obstacles they overcame, and to provide an introspective account of their experiences. The practice of writing a personal journal not only served as a tool for reflection but also helped them adopt a comprehensive perspective on their educational process [ 53 ].

The credibility of the reflective journal prompts was assured by grounding their development in an extensive literature review and expert consultations within the field of healthcare education. This process ensured that the prompts accurately reflected the constructs of interest, facilitating consistent and meaningful student reflections. Content validity was emphasized to ensure the journal prompts were aligned with the study’s objectives and relevant to students’ experiences in healthcare management education. Refinement of these prompts to effectively meet research objectives was facilitated through expert input. A detailed coding scheme was developed, featuring definitions and categories reflecting the study’s aims and insights from the journals. The coding was applied to a subset of journals by the researcher to ensure credibility.

The data were collected from the reflective journals in accordance with the intervention plan outlined in the Instructional Method section. The study carefully complied with several ethical guidelines for research with human subjects. The nature and purpose of the research were fully explained to the students, with particular emphasis on the use of reflective journals to evaluate the intervention plan. The students gave their informed consent and signed consent forms. To ensure confidentiality, participants were informed that all names would be replaced by pseudonyms and all identifying details would be removed from the final research report. They were also explicitly told that the journal entries would be processed anonymously. The research was approved by the college’s Ethics Committee.

Instructional method procedure (intervention plan)

The focus of this study is a required course titled Introducing Quality into the Health System, which had formerly been taught using traditional frontal teaching methods. The study examines the transformation of this course into a course taught using ChatGPT-mediated online guided learning. This innovative learning approach provides learners a comprehensive experience that entails self-directed learning. The approach emphasizes problem-based learning and focuses on identifying ethical dilemmas and analyzing them within organizational contexts. The intervention plan was strategically organized into five primary stages. Each stage comprised a series of carefully constructed steps that were specifically designed to build upon the knowledge and skills acquired in the previous stages, thus ensuring a coherent and cumulative educational progression. Figure  1 summarizes the instructional method.

Initial Familiarization with ChatGPT

At the beginning of the course, students were introduced to ChatGPT to develop their understanding and proficiency with the tool. This involved providing them detailed instructions on effective usage and encouraging them to engage in interactive dialogues with ChatGPT. The aim was to foster a sense of familiarity and ease, thereby facilitating an informal, hands-on learning experience.

Exploratory Analysis of a Dilemma using ChatGPT

In this exploratory stage, students began to examine the topic of hospital accreditation. Through interactions with ChatGPT, they were introduced to the pros and cons of the accreditation process and to the dilemmas posed by following the accreditation guidelines. The issue of accreditation is central to the discourse on how to improve healthcare quality, but it is also fraught with challenges, such as staff shortages and funding issues. Hospitals have had to make significant changes to meet accreditation standards, leading to debates about possible abolition of the accreditation system. While accreditation is crucial for quality control, its associated costs, particularly those related to inspections and the need for additional staff, pose significant challenges. Without proportional funding, compulsory accreditation has placed financial pressures on hospitals, creating a complex dynamic for both the Ministry of Health and healthcare institutions as they navigate the accreditation process.

To explore the topic of accreditation in depth, students were instructed to develop a series of questions to input to ChatGPT aimed at extracting detailed information about the accreditation dilemma. Students engaged with ChatGPT by posing questions and critically analyzing the answers from three perspectives: organizational, healthcare worker, and patient/customer. They iteratively refined their queries to increase precision until they achieved a comprehensive understanding. Following guidelines, they condensed and reorganized the information into a structured paragraph, incorporating the core dilemmas and arguments from each perspective. To meet objectives, students demonstrated digital media skills, including locating and sharing relevant materials, analyzing ChatGPT responses, verifying sources, and assessing content credibility.

Synthesis and Documentation of Concepts Emerging through ChatGPT Interaction

In the third stage, students were required to submit a comprehensive list detailing new concepts, themes, and sub-themes that emerged from their learning experience with ChatGPT. Their submitted list was not limited to the final results, but also included documentation of all stages of their work, including their initial set of questions, their subsequent refinement of these questions, and the process of their development throughout the learning journey. In addition, they were required to provide a final section summarizing the culmination of their exploration and learning process with ChatGPT. This comprehensive approach was designed to demonstrate the students’ engagement and progression with the tool and to highlight their ability to develop their inquiries and synthesize information effectively.

Analytical Structuring of Learning Outcomes

In the fourth stage, students attempted to refine the learning outcomes they had previously generated. Following the established guidelines, their main objective was to identify and highlight the pros and cons of the various arguments related to the dilemmas they had studied, making sure to consider them from different perspectives. The challenge was to present their arguments in a coherent and logical order, for example by comparing budgetary considerations with quality considerations. They were also expected to support each argument with scientific evidence, thereby aligning their analysis with academic accuracy and empirical research. This stage was crucial in developing their ability to critically evaluate and articulate complex issues, particularly in the field of healthcare.

Final project: Integrative Analysis and multidimensional presentation

In the final stage, students developed and presented a final project, building upon their prior work to explore a comprehensive research question or delve into a specific aspect of their study. This included presenting organizational and managerial viewpoints. The choice of format and tools for their project and presentation—ranging from e-posters and slides to video clips, using familiar technologies like PowerPoint and ThingLink—was left to the students. This method fostered diversity and empowered students by allowing them to select their preferred presentation technique. Moreover, the project featured a peer review phase where students critiqued each other’s work through insightful questions and suggestions, enhancing the discussion. This interactive element aimed to bolster critical thinking and collaborative learning.

Summary of instructional method

Reflective Journaling: documenting the Learning Journey

Throughout the semester, students kept a reflective journal, which they submitted at the end of the course. The primary aim of this journal was to document their personal learning experiences. The journal provided a window on their challenges, difficulties and successes they encountered, all viewed through the lens of their own perceptions and experiences.

Data analysis

The present research employed a deductive-inductive method for categorical analysis of the dataset. Integration of these deductive and inductive approaches was essential to facilitate investigation of predefined categories that are grounded in extant literature and theoretical frameworks, as well as to permit the discovery of novel categories that surfaced during the analysis process [ 51 ]. Initially, the deductive stage was conducted, focusing on predefined categories derived from existing literature and theoretical frameworks. Following this, the inductive stage allowed for the identification and development of novel categories based on the data analysis. The inclusion of episodes, thoughts, and feelings expressed by the students in this study serves to reinforce the reliability of the identified themes. The analysis of the reflective journals began with in-depth reading to identify initial themes from students’ narratives. Inductive coding facilitated the identification and development of themes by the researcher, rather than merely allowing them to ‘emerge.’ This active interpretation and organization of the data by the researcher led to a compilation of key insights. After ensuring the reliability and validity of these findings through careful review, the researcher then organized the codes into themes and sub-themes, ensuring they accurately reflected the data and provided a clear narrative of the students’ experiences.

The findings

The researcher’s analysis of the reflective journals actively uncovered five main categories: (1) Use of Literacy Skills; (2) User Experiences with ChatGPT; (3) ChatGPT Information Credibility; (4) Challenges and Barriers when Working with ChatGPT; (5) Mastering ChatGPT Prompting Competencies. Table  1 summarizes the identified categories and subcategories. To further clarify each category, the table includes representative quotations from the data for illustrative purposes. Throughout the manuscript, pseudonyms have been used with quotations. This approach ensures confidentiality and anonymity for all participants.

Use of literacy skills

The category comprising the use of literacy skills, the code refers to instances where participants relate literacy skills such as reading comprehension, searching evaluation of Information, etc., in their interactions with ChatGPT.

It includes three subcategories: Search Strategies and Access to Data in ChatGPT Use; Data Analysis Enhancement with ChatGPT ; and Evaluation of Information in ChatGPT Interactions Search Strategies and Access to Data in ChatGPT Use.

In the reflective journals, the students consistently expressed their high regard for the efficiency and ease of searching for and accessing information through ChatGPT. The chat interface significantly improved the process of retrieving information by removing the necessity to navigate through multiple websites or sources, thereby making the material more accessible. Furthermore, the interface’s user-friendly and accessible content format played a crucial role in significantly enhancing students’ understanding of the material. Shir wrote: The chat was super easy and helpful in making the dilemma clearer for me. It put all the info I needed in one spot, and everything was explained in a way that was simple to understand.

The analysis of the student journals underscored the remarkable proficiency of ChatGPT in rapidly and effortlessly providing information for various tasks. This technology alleviated the necessity for students to delve into multiple sources, offering a direct approach for understanding concepts, interpreting implications, and compiling data for complex issues. ChatGPT’s swift and handy information retrieval supported autonomous learning on the topic. As an accessible and user-friendly tool, it saved considerable time. Moreover, its accessibility and constant availability helped in tailoring learning experiences to fit the learner’s schedule, independent of external factors or intermediaries. ChatGPT’s use of simple, everyday language, coupled with its capacity to deconstruct and elucidate complex concepts, rendered it exceedingly approachable and beneficial for information searches and for enhancing the accessibility of educational content. Lihi also acknowledged the efficacy of ChatGPT in facilitating the rapid acquisition and expansion of her conceptual knowledge. She underscored that the ChatGPT tool obviated the need to consult multiple databases and websites for extracting conceptual information: ChatGPT is really fast and easy to use when you need info on lots of different things. It’s great for finding technical stuff, explaining problems, understanding things better, and getting new ideas on the spot. You don’t even have to go looking for more sources – it’s all right there.

Data synthesis and analysis enhancement with ChatGPT

Analysis of the reflective journals indicates that students found the synthesis, editing, and analysis of content facilitated by ChatGPT to be extremely beneficial. The tool significantly reduced the technical complexity of gathering and synthesizing information from different sources, tasks that had previously been their responsibility. As a result, they were spared the need for synthesizing, editing, and analyzing the raw data, with ChatGPT efficiently performing these functions on their behalf. Meir wrote: ChatGPT really helped us out. It gave us a full picture of the whole process, including the good and bad parts, and how to handle them. We didn’t even need to look at any other info sources at that point .

Evaluation of information in ChatGPT Interaction

The streamlined data collection procedures enabled the students to engage in more advanced learning processes, such as distinguishing between facts and assumptions, differentiating critical from non-critical information, and developing arguments as they advanced to more complex stages. The students observed that although ChatGPT presented data objectively, it did not offer explicit arguments, thus requiring them to actively interpret and formulate their own positions regarding the dilemma and identify the foundational principles for their principal arguments. For example, Miri’s reflections highlighted her need to formulate and develop a stance on the dilemma, which compelled her to engage in critical assessment of the situation:

ChatGPT didn’t really point out which arguments were more important or less important. It kind of listed them all the same way, which made me decide for myself what to focus on. I had to pick the arguments I thought were key and then find evidence to back them up.

Furthermore, the students were asked to support their arguments with evidence from the academic literature, necessitating a thorough evaluation and critical analysis of the information. This process led them to make informed decisions and formulate solutions. In their reflective journals, students documented a cautious approach, emphasizing the need not to simply accept information as it is presented. Instead, they highlighted the importance of thoroughly evaluating the information’s accuracy. Amir similarly addressed this issue, noting his necessity to independently navigate the “thinking part” and acquire the skills to construct strong arguments or effectively employ academic resources: The chat didn’t really help me figure out what’s important and what’s not when I write. It also didn’t teach me how to make strong arguments or how to use academic stuff to back up my points.

User experiences with ChatGPT

This category refers to the qualitative data related to participants’ overall experiences, perceptions, and attitudes towards interacting with ChatGPT. The theme of user experiences is divided into three sub-themes: Time Efficiency using ChatGPT; Accessibility and Availability of ChatGPT; and User-Friendly Dynamics . Overall, analysis of the students’ reflective journals reveals broad agreement about ChatGPT’s user-friendliness and ease of use. Many students noted the chatbot’s intuitive interface and straightforward functionality, which made it accessible to those who may not be tech-savvy. This consensus highlights the effectiveness of ChatGPT as a tool that simplifies information acquisition and supports learning without the typical complexities associated with advanced technological tools.

Time efficiency using ChatGPT

In this sub-category, analysis of the student journals revealed the major time-saving benefits of using ChatGPT for various tasks. ChatGPT successfully eliminated the need for students to sift through numerous sources of information. By providing a straightforward way to understand a concept, grasp its implications, and gather information on complex dilemmas, ChatGPT demonstrated its efficiency in saving students’ time. Riad mentioned the significant time efficiency gained from using the tool, highlighting how it saved him considerable time: You can find out a lot about all sorts of things really quickly. The chat gives you detailed breakdowns and explanations, sorting everything into different arguments and topics; it saves you a lot of time.

Ali also referred to this point: I was not very familiar with the details of accreditation, including its benefits and challenges, but within minutes I was able to grasp its essence and understand the importance of the whole process.

The time efficiency extended not only to data retrieval and collection but also encompassed information synthesis, significantly reducing the amount of time usually required for comprehensive and coherent processing and reformulating of acquired data. Mai observed that the time saved was also because she didn’t need to search for data across multiple sources and combine it together:

The amount of time I save is insane. If I had to search for this stuff on the internet instead of using the chat, it would take me way longer to find an answer. And even after finding it, I’d have to summarize what I found and then rephrase it in my own words, which takes so much time.

Accessibility and availability of ChatGPT

A majority of the students noted that the tool’s immediate accessibility and availability significantly facilitated the personalization of learning approaches. This customization seamlessly interfaced with the unique scheduling needs of each learner, offering flexibility that in traditional learning settings is typically constrained by external factors or intermediaries. Hana highlighted ChatGPT’s anytime, anywhere accessibility through a simple interface, enabling quick and comprehensive responses without the wait for expert assistance: ChatGPT is available to use anytime, anywhere using a simple and convenient interface. This would allow you to get a quick and comprehensive response at any time of the day, without having to wait around for people or experts to help you out.

Lina similarly noted: It’s pretty great how available it is (as long as it’s not too busy…). Any question I have, I get an answer. It saved me a lot of Google searches and reading articles and stuff. I get a quick and clear answer to everything I ask and it’s all super fast.

ChatGPT Information credibility

This category involves instances where participants discuss the credibility, reliability, and trustworthiness of the information provided by ChatGPT. Analysis of the reflective journals showed that interaction with ChatGPT facilitated students’ ability to acquire fundamental knowledge, which could then be expanded upon through subsequent inquiries and verification. Nevertheless, as students proceeded in their tasks, particularly those that required articulating arguments and substantiating their stances on complex dilemmas, they acknowledged the limitations of relying solely on ChatGPT. These limitations focused primarily on concerns about the tool’s credibility in providing sufficiently authoritative information. In this regard, Ofri appreciated ChatGPT’s quick access to information but expressed concerns over its credibility and occasional inaccuracies, leading to unexpected disappointment:

I have found that ChatGPT has a lot of good points. It can quickly give you a lot of information on so many topics and you can really use that information. But I have also learned that this tool has its drawbacks. It is not always right, and it certainly doesn’t always give you things that are based on solid academic facts. Sometimes ChatGPT just makes things up. To be honest, realizing this was a bit of a shock to me.

Students also noted that they were often faced with an overwhelming amount of information, some of which was irrelevant or incorrect, requiring them to evaluate the information and determine its quality. Dalia noted that while ChatGPT provided extensive information initially, aiding in learning about the topic, it also required discernment to distinguish between accurate and less relevant information: In the first stage, the chat gave us a lot of information, which was great because it helped us learn more about the topic. But at the same time, we had to decide which information was really important and accurate and which wasn’t.

Students’ understanding of the limitations of relying solely on the information provided to justify arguments and articulate positions in dilemmas motivated them to examine and assess its reliability. They did so by asking specific questions and consulting established academic references. From the students’ point of view, this careful research and critical evaluation process not only provided them with the opportunity to refine their powers of critical thinking and analysis, it also equipped them with the capacity to critically evaluate the credibility of the information presented. Lina wrote:

I attempted to back up the info I found with academic sources, but then I figured out that the chat isn’t always reliable…. I went through each article that I got results from…to check where is it from, and whether the author actually existed or was just made up… After that, I did another check with other databases. This whole process made me super cautious and thorough in checking everything.

The students expressed unanimous agreement that the need to assess the information provided by the chat forced them to be critical and use evaluation skills. Not only was this a skill they needed to be able to put to good use. It also constituted a challenge in using ChatGPT, as Limor stated that, contrary to reducing critical thinking, proper use of ChatGPT can enhance it by prompting users to reconsider and verify information, despite the challenge:

It might seem that using ChatGPT would make you think less because, well, it’s like chatting to a robot. But actually, if you use it properly and really get into it, it adds a lot to your knowledge and makes you think more broadly and deeper. This is because it makes you think about things over and over again, and double-check the information… it wasn’t easy.

Challenges and barriers in Working with ChatGPT

This category encompasses the various obstacles, difficulties, and limitations encountered by participants while using ChatGPT, including technical issues, comprehension challenges, and frustration. The analysis suggests that despite the students’ widespread agreement on the advantages of using ChatGPT, such as its ease of use, constant availability, and user-friendliness, its accompanying challenges should also be considered. Among these challenges are hesitation in adopting new, cutting-edge technology, difficulties in learning how to use the tool, and language barriers. The language issue was particularly significant, as ChatGPT operates mainly in English, which is not the first language of many of the students. Shir faced difficulties with English translation but viewed it as an opportunity to improve language skills, eventually becoming more comfortable with the chat and reducing reliance on outside translation help:

One big problem I had was writing in English and then translating it to express what I wanted to say. But I decided to take it on as a challenge and use it as a chance to improve my reading and writing in English. Since we didn’t have to use English much, at first it felt like it took forever to understand or read stuff. But gradually, we got the hang of the chat and didn’t need as much help with translating from outside sources.

Some students noted that they also faced some technical issues, revealing the downside of depending exclusively on online tools for studying. For many students, this was their first time using AI including applications like ChatGPT that are built on large language models. As they continued to use it, however, they became more accustomed to it. Ali found initially accessing the GPT chat difficult and, despite its ease of use, experienced issues with site access due to high traffic and occasional freezing, hindering continuous use:

When I first tried the GPT chat for my task, it was a bit tough to get onto the site. But after a while, I noticed that even though the chat is easy to use, it’s got its problems. Sometimes, you can’t even get into the chat because too many people are trying to use it at the same time, and other times, it just freezes up, and you can’t keep using it.

Mastering ChatGPT-Prompting competency

This category involves instances where participants demonstrate proficiency in formulating effective prompts and questions to elicit accurate and relevant responses from ChatGPT. Analysis of the reflective journals revealed that this theme posed a notable challenge for the students, primarily due to their unfamiliarity with the tool. Indeed, they needed to learn how to use the chat effectively to elicit the correct responses and achieve their desired outcomes. Additionally, they encountered challenges in ensuring accuracy and setting the right parameters to establish a reliable and precise database. Despite these obstacles, the students recognized that their efforts to achieve accuracy and their practice of asking repetitive questions were instrumental in developing higher-order thinking skills and being able to organize and manage the required information proficiently. Liya related to this challenge by noted that dealing with inaccurate responses from the model involves clarifying questions with more details, considering alternative answers, and emphasizing the importance of verifying the information received:

Sometimes the model may give you wrong information or answers… to cope with getting answers that are not accurate, you should make your question clearer and add more details. Also think about using different choices of answers. And it is really important to always check the answers you’re getting.

Analysis of the reflective journals showed that systematic demonstration of these activities, along with comprehensive detailing of early learning stages and the cumulative nature of the tasks, provided students the chance to assess and revisit each step retrospectively. This reflective review allowed them to seek explanations for any aspects that were unclear, ask more questions and craft more targeted prompts, and gain a deeper understanding of the entire process. Rim, for example, explained: The chat lets us get information in a series, like being able to ask another question to get a better understanding or clear up something from the first questions we asked. This helped us keep track of everything by linking all our questions together.

Nir noted that the need to aim for accuracy by repeatedly refining the questions really helped in dealing with the assigned tasks effectively:

From my experience with ChatGPT, I have learned that if you want good answers, you have to be really clear about what you are asking. You need to know what you want to achieve with the chat. It is best to give specific instructions to obtain the exact info you need. Also, you should think carefully about the answers you get, making sure the facts are right, and using your own thinking to make wise decisions.

This qualitative study examined the process of introducing and using a pedagogical framework that integrates problem-based learning with the use of ChatGPT among undergraduate healthcare management students. The study also provided a qualitative exploration of their experiences using this technology and assessed how the use of ChatGPT can shift the focus from traditional digital literacy skills to advanced AI literacy skills. It demonstrated how the use of the ChatGPT platform can be managed to encourage the development of critical thinking and evaluation skills through active student engagement. These skills are considered critical for learning and working with AI platforms.

The analysis of students’ reflective journals indicated a perception of the platform as user-friendly. Minichiello et al. [ 54 ] expand the definition of “user experience” beyond mere interaction with user interfaces to include design, information presentation, technological features, and factors related to emotion, personal connection, and experience. Students described their experience with the platform positively, citing it as an incentive for ongoing engagement.

The analysis also showed that the platform’s efficiency was significantly influenced by its high availability and accessibility, which were key factors in its attractiveness to users. This attractiveness was further enhanced by its ease of use. A critical aspect of the platform’s effectiveness was its efficiency in providing key materials in a timely manner, drastically reducing the time required to retrieve information. Users particularly appreciated this aspect of the platform as it streamlined their access to information and significantly improved their learning efficiency. The platform’s ability to deliver relevant information quickly and efficiently was instrumental in its positive reception. In an academic environment where efficient time management and quick access to educational materials are essential, the platform’s ability to meet these needs effectively constituted a notable advantage.

However, students noted initial difficulties and obstacles in utilizing ChatGPT, primarily related to data credibility. These challenges, highlighted in the qualitative data, necessitated the application of critical thinking and conducting various checks to verify the information received. This concern over the credibility of information from AI tools aligns with observations by Mohamad-Hani et al. [ 55 ], who reported similar credibility issues with ChatGPT data among healthcare professionals.

Another significant challenge for the students focused on how to retrieve relevant and accurate information. To this end, they had to refine their question formulation to extract the most relevant and accurate data from the tool. Such challenges have increasingly become a focus of academic attention due to the emerging recognition of the importance of developing prompting skills for effective interaction with platforms such as ChatGPT and other AI tools [ 19 , 20 ].

In terms of digital literacy skills, the findings of this study suggest that basic literacy skills such as locating, retrieving, synthesizing, and summarizing information may become less important as AI systems improve. Yet students still must be trained to evaluate and think critically about AI tools and what they can accomplish, especially since AI technologies like ChatGPT are not always completely trustworthy. Therefore, students need to learn how to evaluate the information these tools provide. These findings also offer some support for the notion that while digital literacy is undeniably recognized as crucial for the 21st century, especially in the healthcare arena [ 36 , 45 ], the definition of digital literacy is changing as technological tools develop. For decades, education focused on developing basic skills. Over time, however, there was a shift toward the cultivation of more complex skills involving information evaluation, synthesis, and assessment [ 56 , 57 ]. Yet as AI continues to penetrate everyday life, there has been a noticeable evolution in the forms of literacy required.

This evolution marks a transition from traditional data digital literacy, which emphasizes a basic understanding and processing of information, to AI digital literacy, which goes beyond mere data consumption to include using digital tools skillfully, understanding the nature of digital content, and effectively navigating the complex digital landscape. This shift reflects the changing demands of a technology-driven society, in which digital literacy is becoming increasingly essential for both personal and professional development [ 58 ]. As AI becomes integrated into different dimensions of work and daily life, especially in the healthcare industry, AI digital literacy will continue to evolve to meet the new demands. This will require a different set of skills, including prompting skills that allow users to better interact with AI tools [ 19 , 20 ].

These results highlight the importance of rethinking the educational use of AI tools such as ChatGPT, potentially leading to changes in future learning curricula. Without the ability to use digital tools, students are liable to fall behind when it comes to adapting to new technologies, thus limiting their ability to learn key skills. Therefore, AI tools must be taught and used in a way that supports students’ holistic learning. These findings align with those of other researchers who focus on the use of the AI platform in education [ 40 , 42 , 43 ]. Such an approach will ensure that students are prepared for the evolving challenges and opportunities of our increasingly digital world. This is especially important in the medical education field, as AI is increasingly being used in different ways to improve the accuracy of disease diagnosis, treatment strategies, and prediction of patient outcomes [ 9 , 10 , 25 , 27 ].

Given that AI technology is still developing and is anticipated to advance and become more widely used [ 21 , 22 ], the need to adapt and acquire new literacy skills is growing. As AI evolves, reliance on traditional basic skills may decline over time, underscoring the importance of learning how to effectively utilize and interact with emerging technologies. Learning to engage with AI tools such as ChatGPT from an early stage in their education can greatly enhance students’ learning experiences. This early exposure will not only provide them with a deeper understanding of these tools. It will also boost their motivation to learn how to use them more effectively, thus highlighting the importance of training students to handle such technologies proficiently. Equally important is the need to guide students through these learning processes to ensure they acquire the necessary skills and knowledge to navigate and utilize AI tools successfully in their educational journey [ 11 ].

Limitations and future research directions

This study utilized a pedagogical framework that integrates problem-based learning with the use of ChatGPT. While the researcher focused on the pedagogical aspect, future research is warranted to compare this digitally supported activity to a non-digital equivalent and examine the impact on students’ literacy and skills. Such a comparison would make it possible to assess what the digital instrument contributes to skill development and to identify any challenges encountered.

The use of this tool across different teaching methods could also be explored to determine whether it is particularly effective for certain types of tasks or requirements. The current study focused on health management. Implementation of this teaching approach in other academic areas should be examined to assess its effectiveness in acquiring competencies in different arenas. The findings of this study highlight the need for further research into the use of AI in learning environments that focus on goal-oriented pedagogy. Such research can help in developing educational strategies that promote the skills essential for lifelong learning.

Conclusions and recommendations

In conclusion, revisiting the research questions in the context of our findings highlights the transformative potential of integrating ChatGPT with problem-based learning in healthcare management education. This study underscores how such integration not only shifts the focus from traditional digital literacy to advanced AI literacy skills but also enhances critical thinking and evaluation capabilities among students. These competencies are indispensable as AI continues to reshape the landscape of healthcare and medical education. AI is emerging as a transformative force that will fundamentally change the global landscape. Although we are still in the early stages of integrating and understanding AI capabilities, its potential to shape our future is clear. Adapting to this digital transformation, especially in healthcare, is crucial [ 4 , 6 ].

Integrating AI into healthcare systems poses significant challenges and raises many unanswered questions [ 9 , 10 ]. These issues require careful consideration and strategic planning to maximize benefits while addressing implementation complexities. The extent and impact of these transformations on the health system and its workforce remain uncertain. However, it is crucial to prepare for these changes at both individual and organizational levels. Educational institutions must update their teaching methods to meet digital demands, recognizing the critical role of educators in developing effective support strategies.

To enable healthcare professionals to integrate AI tools effectively, these tools should be introduced early in education, such as during undergraduate studies or initial professional training [ 9 , 32 , 33 ]. Hands-on experience allows learners to build confidence and understand the tools’ limitations. Additionally, AI tools and especially LLMs such as GPT and their applications, including platforms like ChatGPT, can serve as user-friendly and efficient learning aids, as demonstrated in this research. In addition, researchers should strive to develop innovative pedagogical methods for integrating these tools into different curricula, as exemplified here by the effective use of dilemma-based learning enhanced by ChatGPT. These studies should focus on determining which skills will become redundant and on highlighting essential competencies needed for AI literacy, including prompting, evaluation skills, and critical thinking, all of which are essential for effectively integrating AI and LLMs into medical education and daily practice. Participants in such studies have noted that the acquisition of such skills, particularly in the area of effective prompting, significantly improves the quality of AI responses. Similar to learning a new language, learning to use AI requires precise phrasing and an in-depth understanding of context. Not only will AI skills improve student engagement and comprehension, they will also encourage critical thinking, leading to better educational outcomes. Students who formulate well-structured search queries obtain more accurate responses from AI, which are critical to improving healthcare and learning outcomes.

It is therefore imperative that academia and higher education institutions, including medical education institutions, adopt methods for effectively guiding and training students in using AI. This approach is essential to address the evolving global educational landscape and to embrace the shift in roles. Educators should move from being primarily providers of knowledge to being facilitators of cultural understanding and skill development. Such a shift is essential to promote the transformative evolution of the role of educators in the modern educational context.

Availability of data and materials

Data are available upon request from the Corresponding author.

Counte MA, Howard SW, Chang L, Aaronson W. Global advances in value-based payment and their implications for global health management education, development, and practice. Front Public Health. 2019;6:379. https://doi.org/10.3389/fpubh.2018.00379 .

Article   Google Scholar  

WHO. World Health Organisation. Universal health coverage. 2019. Retrieved from https://www.who.int/news-room/fact-sheets/detail/universal-health-coverage-(uhc) . June 2022.

Naamati Schneider N. Public-private: unequal competition: Israeli public hospitals vs. the private healthcare system following government reforms. Int J Organ Anal. 2021. https://doi.org/10.1108/IJOA-06-2020-2237 .

Deshpande N, Arora VM, Vollbrecht H, Meltzer DO, Press V. eHealth literacy and patient portal use and attitudes: cross-sectional observational study. JMIR Hum Factors. 2023;10:e40105. https://doi.org/10.2196/40105

Naamati Schneider L. Strategic management as an adaptation to changes in the ecosystems of public hospitals in Israel. Isr J Health Policy Res. 2020;9:65. https://doi.org/10.1186/s13584-020-00424-y .

Naamati-Schneider L. The effect of digitalization on service orientation and service perception among Israeli healthcare professionals: a qualitative study. Digit Health. 2023;9. https://doi.org/10.1177/20552076231191892 .

Naamati-Schneider L, Salvatore FP. Sustainability for healthcare organisations and systems: cultivating strategy and governance processes for a better future. In: Vrontis D, Thrassou A, Efthymiou L, Weber Y, Shams SMR, Tsoukatos E, editors. Business for sustainability. 2023; Volume I. pp.227–249. Palgrave studies in cross-disciplinary business research, in association with EuroMed Academy of Business. Cham: Palgrave Macmillan. https://doi.org/10.1007/978-3-031-37361-9_11 .

Naamati-Schneider L, Zaks O. Public hospitals in crisis: managerial and strategic adaptation. In: Vrontis D, Thrassou A, Weber Y, Shams SMR, Tsoukatos E, Efthymiou L, editors. Business under crisis: volume II. Palgrave Macmillan; 2022. p. 43–64. https://doi.org/10.1007/978-3-030-76575-0_3 .

Ali O, Abdelbaki W, Shrestha A, Elbasi E, Alryalat MAA, Dwivedi YK. A systematic literature review of artificial intelligence in the healthcare sector: benefits, challenges, methodologies, and functionalities. J Innov Knowl. 2023;8(1):100333.  https://doi.org/10.1016/j.jik.2023.100333

Manne R, Kantheti SC. Application of artificial intelligence in healthcare: chances and challenges. Curr J Appl Sci Technol. 2021;40(6):78–89. https://doi.org/10.9734/cjast/2021/v40i631320 .

Breeding T, Martinez B, Patel H, et al. The utilization of ChatGPT in reshaping future medical education and learning perspectives: a curse or a blessing? Am Surg. 2024;90(4):560–6. https://doi.org/10.1177/00031348231180950 .

Boscardin CK, Gin B, Golde PB, Hauer KE. ChatGPT and generative artificial intelligence for medical education: potential impact and opportunity. Acad Med. 2024;99(1):22–7. https://doi.org/10.1097/ACM.0000000000005439 .

Tinmaz H, Fanea-Ivanovici M, Baber H. A snapshot of digital literacy. Libr Hi Tech News. 2023;40(1):20–3. https://doi.org/10.1108/LHTN-12-2021-0095 .

Sallam M. ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthc. 2023;11(6):887. https://doi.org/10.3390/healthcare11060887 .

Buchholz BA, DeHart J, Moorman G. Digital citizenship during a global pandemic: moving beyond digital literacy. J Adolesc Adult Lit. 2020;64(1):11–7. https://doi.org/10.1002/jaal.1076 .

Kuatbekov A, Vershitskaya E, Kosareva I, Ananishnev V. E-learning as a basis for the development of media competences in students. J Inform Sci. 2023;49(4):1111–25.

Morgan A, Sibson R, Jackson D. Digital demand and digital deficit: conceptualising digital literacy and gauging proficiency among higher education students. J High Educ Policy Manag. 2022;44(3):258–75. https://doi.org/10.1080/1360080X.2022.2030275 .

van Laar E, van Deursen AJ, van Dijk JA. Developing policy aimed at 21st-century digital skills for the creative industries: an interview study with founders and managing directors. J Educ Work. 2022;35(2):195–209. https://doi.org/10.1080/13639080.2022.2036710 .

Eager B, Brunton R. Prompting higher education towards AI-augmented teaching and learning practice. J Univ Teach Learn Pract. 2023;20(5):02. https://doi.org/10.53761/1.20.5.02 .

Kasneci E, Seßler K, Küchemann S, Bannert M, Dementieva D, Fischer F, Kasneci G. ChatGPT for good? On opportunities and challenges of large language models for education. Learn Indiv Differ. 2023;103:102274. https://doi.org/10.1016/j.lindif.2023.102274 .

He N, Yan Y, Wu Z, Cheng Y, Liu F, Li X, Zhai S. ChatGPT-4 significantly surpasses GPT-3.5 in drug information queries. J Telemed Telecare. 2023;2023:1–3. https://doi.org/10.1177/1357633X231181922 .

Javaid M, Haleem A, Singh RP. ChatGPT for healthcare services: an emerging stage for an innovative perspective. BenchCouncil Trans Benchmarks Stand Eval. 2023;3(1):100105. https://doi.org/10.1016/j.tbench.2023.100105 .

Haleem A, Javaid M, Singh RP. An era of ChatGPT as a significant futuristic support tool: a study on features, abilities, and challenges. BenchCouncil Trans Benchmarks Stand Eval. 2022;2(4):100089.  https://doi.org/10.1016/j.tbench.2023.100089

Fuchs K. Exploring the opportunities and challenges of NLP models in higher education: is ChatGPT a blessing or a curse? Front Educ. 2023;8:8. https://doi.org/10.3389/feduc.2023.1166682 .

Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):e000101. https://doi.org/10.1136/svn-2017-000101 .

Tekkeşin Aİ. Artificial intelligence in healthcare. Anatol J Cardiol. 2019;22(1):8–9. https://doi.org/10.14744/AnatolJCardiol.2019.28 .

Jimma BL. Artificial intelligence in healthcare: a bibliometric analysis. Telemat Inform Rep. 2023:100041.‏ https://doi.org/10.1016/j.teler.2023.10004 .

Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–31.

Rong G, Mendez A, Assi EB, Zhao B, Sawan M. Artificial intelligence in healthcare: review and prediction case studies. Engineering. 2020;6(3):291–301. https://doi.org/10.1016/j.eng.2019.08.015 .

Sqalli MT, Al-Thani D. AI-supported health coaching model for patients with chronic diseases. In: 2019 16th International Symposium on Wireless Communication Systems (ISWCS). IEEE; 2019. p. 452–456. https://doi.org/10.1109/ISWCS.2019.8877113 .

Matheny ME, Whicher D, Thadaney Israni S. Artificial intelligence in health care: a report from the national academy of medicine. JAMA. 2020;323(6):509–10. https://doi.org/10.1001/jama.2019.21579 .

York TJ, Raj S, Ashdown T, Jones G. Clinician and computer: a study on doctors’ perceptions of artificial intelligence in skeletal radiography. BMC Med Educ. 2023;23(1):1–10. https://doi.org/10.1186/s12909-022-03976-6 .

Eysenbach G. The role of ChatGPT, generative language models, and artificial intelligence in medical education: a conversation with ChatGPT and a call for papers. JMIR Med Educ. 2023;9:e46885. https://doi.org/10.2196/46885

Han Z, Battaglia F, Udaiyar A, Fooks A, Terlecky SR. An explorative assessment of ChatGPT as an aid in medical education: use it with caution. medRxiv. 2023.‏ https://doi.org/10.1101/2023.02.13.23285879 .

Das D, Kumar N, Longjam LA, Sinha R, Roy AD, Mondal H, Gupta P. Assessing the capability of ChatGPT in answering first-and second-order knowledge questions on microbiology as per competency-based medical education curriculum. Cureus. 2023;15(3):e36034. https://doi.org/10.7759/cureus.36034 .

Thurzo A, Strunga M, Urban R, Surovková J, Afrashtehfar KI. Impact of artificial intelligence on dental education: a review and guide for curriculum update. Educ Sci. 2023;13:150. https://doi.org/10.3390/educsci13020150 .

Veikkolainen P, Tuovinen T, Jarva E, Tuomikoski AM, Männistö M, Pääkkönen J, et al. eHealth competence building for future doctors and nurses–attitudes and capabilities. Int J Med Inform. 2023;169:104912. https://doi.org/10.1016/j.ijmedinf.2022.104912 .

Baro EE, Obaro OG, Aduba ED. An assessment of digital literacy skills and knowledge-based competencies among librarians working in university libraries in Africa. Digit Libr Perspect. 2019;35(3/4):172–92. https://doi.org/10.1108/DLP-04-2019-0013 .

Falloon G. From digital literacy to digital competence: the teacher digital competency (TDC) framework. Educ Tech Res Dev. 2020;68:2449–72. https://doi.org/10.1007/s11423-020-09767-4 .

Polizzi G. Digital literacy and the national curriculum for England: learning from how the experts engage with and evaluate online content. Comput Educ. 2020;152:103859.  https://doi.org/10.1016/j.compedu.2020.103859

Tynes BM, Stewart A, Hamilton M, Willis HA. From google searches to russian disinformation: adolescent critical race digital literacy needs and skills. Int J Multicult Educ. 2021;23(1):110–30. https://doi.org/10.18251/ijme.v23i1.2463 .

Ahmed ST, Roche T. Making the connection: examining the relationship between undergraduate students’ digital literacy and academic success in an English medium instruction (EMI) university. Educ Info Technol. 2021;26(4):4601–20. https://doi.org/10.1007/s10639-021-10443-0 .

Musi E, Aloumpi M, Carmi E, Yates S, O'Halloran K. Developing fake news immunity: fallacies as misinformation triggers during the pandemic. Online J Commun Media Technol. 2022;12(3). https://doi.org/10.30935/ojcmt%2F12083 .

Pangrazio L, Sefton-Green J. The social utility of ‘data literacy.’ Learn Media Technol. 2020;45(2):208–20. https://doi.org/10.1080/17439884.2020.1707223 .

Mehoudar O. Health literacy, and eHealth literacy: access to health information as a key to equal opportunity in society. Kidum Briut Isr. 2014;5:25–34 (Hebrew).

Google Scholar  

Alt D, Naamati-Schneider L, Meirovich A. Future problem-solving practiced during COVID-19: implications for health management students’ e-health literacy identity. Front Psychol. 2022;13:829243. https://doi.org/10.3389/fpsyg.2022.829243 .

Kaper MS, Reijneveld SA, van Es FD, de Zeeuw J, Almansa J, Koot J, de Winter AF. Effectiveness of a comprehensive health literacy consultation skills training for undergraduate medical students: a randomized controlled trial. Int J Environ Res Public Health. 2019;17(1):81. https://doi.org/10.3390/ijerph17010081 .

Nguyen HT, Do BN, Pham KM, Kim GB, Dam H, Nguyen TT, Nguyen T, Nguyen YH, Sørensen K, Pleasant A, Duong TV. Fear of COVID-19 scale-associations of its scores with health literacy and health-related behaviors among medical students. Int J Environ Res Public Health. 2020;17(11):4164. https://doi.org/10.3390/ijerph17114164 .

TerKonda SP, Fish EM. Artificial intelligence viewed through the lens of state regulation. Intell Based Med. 2023:100088. https://doi.org/10.1016/j.ibmed.2023.100088 .

Yin RK. Case study research design and methods. 5th ed. California: Sage; 2014.

Creswell JW. Educational research: planning, conducting, and evaluating quantitative and qualitative research. 4th ed. Nebraska-Lincoln: Merrill; 2012.

Tripto J, Ben-Zvi Assaraf O, Snapir Z, Amit M. The ‘What is a system’ reflection interview as a knowledge integration activity for high school students’ understanding of complex systems in human biology. Int J Sci Educ. 2016;38(4):564–95. https://doi.org/10.1080/09500693.2016.1150620 .

Zohar A, Barzilai S. A review of research on metacognition in science education: current and future directions. Stud Sci Educ. 2013;49:121–69. https://doi.org/10.1080/03057267.2013.84726 .

Minichiello A, Hood JR, Harkness DS. Bringing user experience design to bear on STEM education: a narrative literature review. J STEM Educ Res. 2018;1:7–33. https://doi.org/10.1007/s41979-018-0005-3 .

Mohamad-Hani T, Aljamaan F, Malki KH, Alhasan K, Altamimi I, Aljarbou R, Al-Eyadhy A. ChatGPT and the future of digital health: a study on healthcare workers’ perceptions and expectations. Healthcare. 2023;11(13):1812. https://doi.org/10.3390/healthcare11131812 .

Alt D, Raichel N. Enhancing perceived digital literacy skills and creative self-concept through gamified learning environments: insights from a longitudinal study. Int J Educ Res. 2020;101:101561. https://doi.org/10.1016/j.ijer.2020.101561 .

UNESCO. Digital skills critical for job and social inclusion. 2018. Retrieved from https://en.unesco.org/news/digital-skills-critical-jobs-and-social-inclusion .

Kreinsen M, Schulz S. Towards the triad of digital literacy, data literacy, and AI literacy in teacher education: a discussion in light of the accessibility of novel generative AI. A position paper. 2023. https://doi.org/10.1177/0163443710393382 .

Olmos-Vega FM, Stalmeijer RE, Varpio L, Kahlke R. A practical guide to reflexivity in qualitative research: AMEE Guide No. 149. Med Teach. 2023;45(3):241–51.

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Offering extended use of the contraceptive implant via an implementation science framework: a qualitative study of clinicians’ perceived barriers and facilitators

• Nicole Rigler 1 ,
• Gennifer Kully 2 , 3 ,
• Marisa C. Hildebrand 2 ,
• Sarah Averbach 2 , 3 &
• Sheila K. Mody 2  

BMC Health Services Research volume  24 , Article number:  697 ( 2024 ) Cite this article

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Metrics details

The etonogestrel contraceptive implant is currently approved by the United States Food and Drug Administration (FDA) for the prevention of pregnancy up to 3 years. However, studies that suggest efficacy up to 5 years. There is little information on the prevalence of extended use and the factors that influence clinicians in offering extended use. We investigated clinician perspectives on the barriers and facilitators to offering extended use of the contraceptive implant.

Using the Consolidated Framework for Implementation Research (CFIR), we conducted semi-structured qualitative interviews. Participants were recruited from a nationwide survey study of reproductive health clinicians on their knowledge and perspective of extended use of the contraceptive implant. To optimize the diversity of perspectives, we purposefully sampled participants from this study. We used content analysis and consensual qualitative research methods to inform our coding and data analysis. Themes arose deductively and inductively.

We interviewed 20 clinicians including advance practice clinicians, family medicine physicians, obstetrician/gynecologist and complex family planning sub-specialists. Themes regarding barriers and facilitators to extended use of the contraceptive implant emerged. Barriers included the FDA approval for 3 years and clinician concern about liability in the context of off-label use of the contraceptive implant. Educational materials and a champion of extended use were facilitators.

Conclusions

There is opportunity to expand access to extended use of the contraceptive implant by developing educational materials for clinicians and patients, identifying a champion of extended use, and providing information on extended use prior to replacement appointments at 3 years.

Peer Review reports

The etonogestrel contraceptive implant is currently approved by the U.S. Food and Drug Administration (FDA) for 3 years of continuous use for the prevention of pregnancy [ 1 ]. However, there is evidence to support its use for up to 5 years while maintaining a low risk of pregnancy [ 2 , 3 , 4 ]. The off-label use of the contraceptive implant past its FDA-approved duration and up to 5 years is known as extended use. Importantly, the FDA supports off-label use of marketed drugs and medical devices so long as there is strong relevant published evidence [ 5 ]. Off-label use such as extended use of the contraceptive implant is common with many other reproductive devices and medications, including misoprostol for labor induction, the copper intrauterine device (IUD) for emergency contraception, and, prior to its recent FDA-approval for extended use, the 52 mg levonorgestrel (LNG) IUD for pregnancy prevention. The 52 mg LNG IUD was previously FDA-approved for 5 years, however strong published evidence demonstrated longer efficacy up to 8 years, leading clinicians to counsel on extended use and eventually contributing to updated federal guidelines [ 6 , 7 ].

Though there are clinicians who counsel patients on extended use of the contraceptive implant, many patients still undergo implant replacement after only 3 years of use [ 8 , 9 ]. Continuation rates of the contraceptive implant after 1 and 2 years of use is estimated to be at 81.7% and 68.7%, with the most common reason for early discontinuation prior to 3 years being changes to bleeding pattern [ 10 , 11 , 12 , 13 ]. Ali et al. report the most common reasons that patients decided to stop implant use in years 4 and 5: unspecified personal reasons, desired fertility, bleeding problems, and other medical reasons [ 4 ]. Additionally, a recent nationwide, web-based survey amongst a diverse group of reproductive health clinicians investigated the barriers and facilitators regarding extended use of the contraceptive implant up to 5 years [ 14 ]. The most common barriers found in the study were provider concerns about pregnancy risk and the current FDA approval for only 3 years of use. The key facilitators included strong published evidence supporting extended use and patient and clinician education on extended use. Other than these studies, the patient and clinician factors that facilitate and hinder widespread implementation of extended use of the contraceptive implant have not been explored.

Increasing implementation of extended use of the contraceptive implant across practice settings may decrease unnecessary procedures, devices, healthcare visits, and could improve access to, and satisfaction with, the contraceptive implant. Long-acting reversible contraceptive (LARC) methods such as the contraceptive implant and LNG IUD have significantly higher continuation and approval rates and are more efficacious at preventing pregnancy than non-LARC methods such as oral contraceptive pills and depot medroxyprogesterone acetate injection [ 12 , 15 , [ 16 ]. Given the continued high rates of unintended pregnancies in the United States and the consequential increase in healthcare costs and poor outcomes secondary to pregnancy complications, efficacious pregnancy prevention is an important public health objective and cost-saving measure [ 17 ].

Using a qualitative approach guided by an implementation science framework, the Consolidated Framework for Implementation Research (CFIR), [ 18 ] we sought to explore clinician perspectives on extended use of the contraceptive implant up to 5 years as well as the perceived barriers and facilitators for clinicians to offer extended use.

We conducted semi-structured interviews with 20 clinicians including obstetrics and gynecology generalists, family medicine physicians, complex family planning sub-specialists, and advanced practice clinicians. We recruited interview participants from a nationwide, web-based survey that assessed the prevalence of extended use of the contraceptive implant [ 17 ]. This study recruited respondents through email listservs for the Fellowship in Complex Family Planning, the Ryan Residency Training in Family Planning Program, women’s health nurse practitioners, and family medicine physicians, as well as private social media groups for obstetrician-gynecologists. The total reach of the survey was unknown, however, the study had a survey completion rate of 66.6% ( n  = 300/450). Of the 300 completed surveys, 290 respondents indicated their interest in being interviewed (96.7%).

Among the survey respondents, we invited 24 clinicians to participate in interviews, yielding an 83.3% response rate. We selectively recruited interview participants to enrich our sample, specifically focusing on clinician type, practice setting, and region of practice within the United States (U.S.). We also selected interview participants based on whether they always, sometimes, or never counsel on extended use to investigate a broad range of perspectives. For this study, offering extended use is defined as counseling on use past the current FDA-approved duration of 3 years and up to 5 years of use. Offering extended use can occur at any clinical encounter, including insertion appointments, replacement and removal appointments at or before 3 years, and general reproductive health appointments. Clinicians who always offer extended use were defined as those who counsel on extended use to patients who are considering or currently have the contraceptive implant. Clinicians who sometimes offer extended use were defined as those who counsel on extended use, but only to particular patients based on patient-specific factors such as body mass index or insurance coverage. Clinicians who never offer extended use were defined as those who never counsel on use of the contraceptive implant past 3 years of use.

The interview guide was created utilizing an implementation science framework that identifies factors for effectively enacting interventions [ 18 ]. The Consolidated Framework for Implementation Research (CFIR) is organized into 5 major domains: characteristics of the intervention, individual characteristics, inner setting, outer setting, and the process of implementation. The first domain, intervention characteristics, relates to the inherent qualities of the intervention, such as pharmacologic properties and side effects of the contraceptive implant when used up to 5 years. Individual characteristics relates to the roles and characteristics of individual patients and clinicians interacting with the intervention, such as educational background and type of insurance coverage. The inner setting domain assesses the internal setting in which an intervention will be implemented (i.e., clinic type, culture, and policies). The broader context in which an intervention will be implemented, including national policies and social norms is evaluated within the outer setting domain. Finally, the process of implementation domain explores the activities and strategies used to implement the intervention, such as educational materials or clinician and staff trainings on extended use.

We designed the interview guide around these specific domains with questions that aimed to identify targeted strategies to support successful implementation. The complete interview guide is in Appendix A . The interview guide was designed with input from clinicians who regularly prescribe contraception, including extended use of the contraceptive implant, as well as CFIR and implementation science experts. The Human Research Protection Program at our institution approved the study.

A single research team member conducted semi-structured interviews via secure video conference between July and August 2021. Interview participants provided informed consent. All participants were asked a full set of open-ended questions based on the interview guide, with focused follow-up questions to further investigate potential themes or to clarify points. All interviews were audio recorded, then transcribed. For data analysis, we used a content analysis approach to identify concepts and patterns within the dataset [ 19 ]. Themes arose deductively and inductively, with deductive themes identified from the CFIR domains and inductive themes arising from interview insights. Consensual qualitative research methods informed both our data analysis and coding process [ 20 ]. Three authors were involved in the thematic coding of the transcripts. Initially, 5 transcripts were independently coded then checked for inter-coder reliability. Any disagreements were discussed, and a consensus was achieved. The remaining transcripts were then coded by one of the three authors. Once all interviews were coded, major themes and representative quotes were identified. The research team utilized ATLAS.ti for analysis [ 21 ].

Between July and August 2021, we interviewed 20 clinicians from a variety of clinical settings, regions, and women’s health professions, achieving the intended diversity of perspectives (Table  1 ). Among participants, 7 (35.0%) always, 8 (40.0%) sometimes, 5 (25.0%) never offer extended use of the contraceptive implant (Table 2 ).

Characteristics of the intervention

We found that changes to bleeding pattern in or after the third year of use was a barrier to clinicians offering extended use of the contraceptive implant. The participants in this study noted that perceived increases in the irregularity or frequency of a patient’s bleeding makes extended use of the implant difficult for patients to accept. One clinician noticed that some patients correlate changes in their bleeding pattern with a perceived decrease in the efficacy of their implant:

"People who do start noticing changes in bleeding pattern […] [and] associating that with, ‘Oh, my implant is wearing out or becoming expired. I need to get this changed out."

-Complex Family Planning Specialist, Southwest, Academic Setting, sometimes offers extended use

The same clinician discussed that more research on bleeding patterns in the extended use period and potential treatments for implant-associated irregularities could be a facilitator of extended use:

"For bleeding, I think it would be awesome if there is a research study, looking at use of OCPs [oral contraceptive pills] to manage bleeding near the end of the use of an implant or near that three-year mark,, […] So that we could give people… Honestly, either a natural history or a, ‘Here’s how you can manage that if you do want to keep using your implant longer.’"

- Complex Family Planning Specialist, Southwest, Academic Setting, sometimes offers extended use

Information on the bleeding pattern in years 4 and 5 of use and how clinicians can address irregular bleeding during implant use may increase acceptability of extended use.

Individual characteristics

We found that insurance impacts whether a clinician offers extended use:

"I do sometimes have patients saying, ‘I might be changing jobs or I’m going to be turning 27 or whatever.’ And so insurance is a barrier and so they’re like, ‘I want the new one while I still have this insurance.’"

- Family Medicine Physician, Midwest, Community Setting, sometimes offers extended use

Many participants agreed with this concept and stated that acceptability of extended use depends on a patient’s perception of their future insurance status. Clinicians observed that if a patient believes they will have coverage for a replacement or removal in the future, they are more likely to pursue extended use of their implant. Conversely, one clinician discussed how lack of current insurance coverage could be a facilitator of extended use:

"So, I would generally offer extended use to people that didn’t have insurance and would have to self-pay. I would like go through the data with them so they wouldn’t have to pay like $1,000 to get a new implant because it could work another year, or people that were concerned about changing side effects at that time."

- Obstetrician-Gynecologist, Southwest, Academic Setting, sometimes offers extended use

Overall, clinicians perceived that patients’ concerns about current and future insurance coverage may affect acceptance of extended use.

Inner setting

This study found that having a champion of extended use at a clinician’s home or affiliate institution was a facilitator of extended use. Most clinicians in the study stated that it is or would be helpful to have someone who worked with them clinically that was knowledgeable on the data about extended use. When asked which factor would promote extended use of the implant the most, this clinician stated:

"…having a champion who is really ready to present the evidence, because the evidence can be there, but people don’t have time to read it. If it’s not brought to them, they’re not really going to know about it."

- Obstetrician-Gynecologist, West Coast, Community Setting, does not offer extended use

Potential champions identified were physicians, nurses, medical directors, or other clinicians in leadership positions, but participants generally believed that the position should be held by someone who is passionate about contraception, highly familiar with the specific setting, and knowledgeable about the clinical studies on extended use.

A barrier noted by a few participants was the effect of discordant counseling by different clinicians, sometimes within the same clinic, on acceptability of extended use:

"I mean, I guess like getting everyone on the same page, like in your practice can be a barrier. Especially in the practice I’ve been at, which like I said was in a state that was very litigious, so people weren’t always willing to like go outside guidelines that were… So getting your whole group on the same page so patients get like a more consistent message."

- Obstetrician-Gynecologist, Southwest, Academic Setting, sometimes offers extended use.

Participants discussed that it is important for clinician teams to relay a cohesive message to patients, especially in settings where patients may see multiple clinicians for their contraceptive care.

Outer setting

Lack of FDA approval for extended use was identified as barrier by many clinicians, and some clinicians counseled patients only on the FDA-approved duration of the contraceptive implant:

"So, generally in our practice we don’t really talk about extended use. We say this is FDA approved for three years."

- Advanced Practice Clinician, Southeast, Community Setting, sometimes offers extended use.

Even clinicians who do offer extended use of the implant noted that off-label use can be confusing to patients, making it difficult to counsel on extended use:

"So I have patients all the time, who’ll say, ‘Well, what do you mean I can keep X, Y or Z in for an extra year?’ And I’ll say, ‘We have big studies that tell us that this is an okay thing to do.’ But that just feels weird. People don’t necessarily understand the role of the FDA or sort of how it works. And so it’s something like extended use just might be a really such a foreign concept. Right? It’s so far outside. But I think that there are also, there are lay outlets that cover this stuff. So it’s not that it’s impossible to access. It’s just that the patient has to be interested just like the provider has to be interested."

- Complex Family Planning Specialist, East Coast, Academic Setting, sometimes offers extended use.

Clinicians also observed that certain clinics must follow official guidelines without the flexibility to offer extended use, regardless of a clinician’s perspective or willingness to counsel on extended use. Interestingly, patient confusion as well as mistrust of the healthcare system may impact patient acceptability of extended use in the context of a three-year FDA-approved duration:

"The other thing is the FDA approval because the box says three years, but then like I tell people, you can take it out in five years. And then they don’t believe… Like who is right. Is it my doctor who’s getting in front of me right or the box, right?"

- Family Medicine Physician, West Coast, Community Setting, always offers extended use.

This clinician noted that a disconnect between a clinician’s counseling and prescription information may lead patients to be confused about the recommendation for extended use.

Another barrier mentioned by a few participants was provider concern about liability in the event of an unintended pregnancy. Participants discussed fear of both legal and interpersonal repercussions of unintended pregnancy after counseling on off-label use of a contraceptive device:

"Even though there’s a slim chance that a patient would get pregnant on Nexplanon [the contraceptive implant], I feel like if we were to say, ‘Yeah, you can use it beyond the four years,’ and they come up and they get pregnant, they’re that 1% chance that gets pregnant, I feel like there could be a little bit of blame laid on us if we were to tell them that they’re able to it beyond the three years when the label doesn’t say that yet."

- Advanced Practice Clinician, Southeast, Private Practice, does not offer extended use.

Some participants felt that they would “have no ground to stand on” in the event of a lawsuit (OBGYN Physician, Midwest, Private Practice), making them concerned about the possibility of increased liability in counseling on off-label use without FDA approval.

Interestingly, multiple clinicians also discussed abortion restrictions in the United States as influencing patients in their decision to pursue extended use or not:

"In the past four years [2017–2021] have also had a lot of patients express concern about the administration. And so wanting to kind of be as current as they can be with their devices and so potentially exchanging them sooner than they need."

- Complex Family Planning Specialist, West Coast, Academic Setting, always offers extended use.

Clinicians observed that patients are noticing and reacting to abortion restrictions when making their contraceptive decisions, which may impact the widespread implementation of extended use.

Process of implementation

Many clinicians reported that a barrier to implementing extended use was patient preference for removal when they are already in clinic for a scheduled removal or replacement procedure, regardless of being counseled on extended use at that time:

“’Oh, I’m already here. I’m approved. Let’s just go ahead and get it done.’ So there’s probably not a whole lot you can do about that either, once they’re already in the clinic, and have their mind set on it.”

- Obstetrician-Gynecologist, Southeast, Academic Setting, does not offer extended use.

Many participants in this study noted that patients have made logistical arrangements prior to their appointments including paid time off, childcare, or prior authorization. It can be difficult for clinicians to offer extended use within this context, therefore counseling is better done prior to a patient coming in for a replacement appointment.

A perceived facilitator of extended use that was mentioned often was clear, concise clinician educational services or materials that illustrates existing data on efficacy and risks. Clinicians believed that this education could be in the form of continued medical education, targeted trainings, or written summaries of relevant studies, data, and recommendations. One consistency across interviews was that education on extended use must be integrated into regular practice and be easily understood by busy clinicians:

"I think that when we get a pamphlet or a brochure or a one page, something that just has everything condensed so it’s a really quick, oh, okay, this is something that we can be offering patients. And these are the reasons why it would be a benefit to them, and these are the patients that maybe would fall out of not offering this to. I think because of how busy we are, that’s the best way for us to make change."

- Advanced Practice Clinician, Southwest, Academic Setting, does not offer extended use.

Participants reported that these resources should be widely distributed beyond the complex family planning and obstetrician-gynecology community to increase accessibility to extended use.

Another potential facilitator identified was effective patient educational materials such as flyers that state the 5-year efficacy of the contraceptive implant, though producing these might require FDA approval. Participants in this study report that patients rely on clinicians to provide information on the efficacy and duration of their contraceptive implant. However, it is difficult for patients to accept extended use when there are inconsistencies across multiple sources of information:

"I mean, if online, there was information where it said you can keep it in for three to five years and they’re able to back that up. You know, people like to do their own research. I think that would be helpful, versus it says everywhere three, three, three, three, three, and then you’re the only person telling them something different, then it’s a little more tricky."

- Obstetrician-Gynecologist, West Coast, Community Setting, does not offer extended use.

Overall, participants in this study expressed that it would be helpful to have easily understood information for clinicians and patients that explained the evidence for extended use.

Our results demonstrate that there is an opportunity to increase widespread implementation of extended use through multiple interventions. Clinicians reported that patients prefer to have their implants replaced when they are already in clinic for the procedure. Therefore, intervening prior to replacement appointments at 3 years in the form of telemedicine visits or notifications from scheduling staff may make extended use of the contraceptive implant more acceptable to patients. Further, clinician and patient education on extended use that is easily understood and widely disseminated would likely increase use of the contraceptive implant up to 5 years.

The implementation of extended use of the contraceptive implant up to 5 years likely decreases healthcare costs secondary to fewer procedures and unintended pregnancies, and expands reproductive choices for patients seeking contraception. It has been found that clinicians who offer extended use state that most of their patients accept extended use when it is offered [ 14 ]. However, the reasons why a patient may or may not accept extended use are unclear, but may include changes in bleeding and concerns about use past the FDA-approved duration. Research on bleeding patterns in the extended use period may facilitate counseling and give patients a better expectation of possible changes they may see in years 4 and 5. Additionally, research on the patient perspective and acceptability of using the contraceptive implant past its FDA-approved timeframe is needed.

This study focused on clinicians and their perspectives on extended use. However, it is important to note that patients may be fully informed about extended use and choose to replace their implant at or before 3 years of duration. All discussions regarding contraception, including extended use of the implant, should always occur within a patient-centered and shared decision-making model. Widespread offering of extended use may allow for more patients to make fully informed decisions about the duration and use of their contraceptive devices, therefore expanding reproductive choice and agency in addition to potentially sparing patients from unnecessary procedures and extra healthcare costs.

Interestingly, although there are data to reflect high implant efficacy in years 4 and 5, [ 2 , 3 , 4 ] some participants in this study believe there is increased liability in counseling on off-label use without FDA approval. Importantly, off-label use is common among reproductive clinicians and is protected by the FDA if there is strong published evidence supporting off label use [ 5 ]. Additionally, the Society of Family Planning supports extended use of the contraceptive implant up to 5 years [ 22 ]. The FDA requires implant training for clinicians before they can insert or remove the implant. This training includes the FDA product labeling indicating the maximum duration of use for pregnancy prevention as three years [ 1 ]. It is possible that clinician training and product labels that advertise a 3-year duration dissuade clinicians from offering extended use of the contraceptive implant due to concerns about legal repercussions in the event of an unintended pregnancy with extended use. Therefore, organization- or systems-level guidelines, policy changes, and trainings in support of extended use may allow clinicians to feel comfortable offering off-label use of the implant. Additionally, FDA approval of the contraceptive implant to 5 years would likely greatly facilitate implementation of extended use.

Changing the FDA label to reflect extended use can be expensive, and contraceptive companies may not be incentivized to change the label. However, increasing the FDA approval of the contraceptive implant would allow for companies to have a longer-acting contraceptive device that is more directly comparable to other LARC devices such as the 52 mg LNG IUD that can be used for up to 8 years. If FDA approval for 5 years of use were to occur, it is not known if the barriers described in this study would continue to apply. However, it is likely that the facilitators of extended use from this study would support implementation of extended use irrespective of the federally approved duration.

One strength of the study is the national sample and the diversity of clinician types and settings. There is also representation of clinicians who consistently offer extended use and those who do not offer extended use. Another strength of this study is that it was designed utilizing a framework focusing on implementation, thus yielding results that can be used to create effective interventions.

Limitations of this study include the small sample size and selection bias from recruiting from a prior study that utilized listservs and social media. Additionally, we recruited from a population that was specifically interested in family planning and identified mostly as Caucasian and female. Because of this, our results may not be generalizable to the national population of clinicians who offer contraceptive implant services. However, our direct selection of participants who only sometimes or do not offer extended use allowed us to hear diverse perspectives regardless of prior knowledge or interest in extended use. Another limitation is that we did not ask advanced practice clinicians what their specific training was (i.e., nurse practitioner or physician’s assistant). As the training for advanced practice clinicians can vary greatly, our results may not be generalizable to all advanced practice clinicians.

In conclusion, this study describes the barriers and facilitators to widespread implementation of extended use of the contraceptive implant. These results offer new perspectives and potential strategies to increase widespread implementation of extended use of the contraceptive implant up to 5 years of use. Based on our findings, there is opportunity to expand access to extended use by developing educational materials for clinicians and patients, identifying a champion of extended use, and counseling on extended use prior to removal appointments at 3 years. Of note, these results should be viewed in the context of recent policy access issues regarding reproductive health and used to support patient-centered contraceptive choices, regardless of a patient’s decision to extend use of their contraceptive implant up to 5 years. It is important that clinicians and patients utilize shared decision making when discussing extended use of the contraceptive implant.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to being stored in a private, HIPAA-compliant database, but are available from the corresponding author on reasonable request.

Abbreviations

Consolidated Framework for Implementation Research

Food and Drug Administration

CoIntrauterine device

• Long-acting reversible contraception

Levonorgestrel

Obstetrician-Gynecologist

United States

Nexplanon® Prescribing Information. Organon. 2021. https://www.organon.com/product/usa/pi_circulars/n/nexplanon/nexplanon_pi.pdf . Accessed 20 Feb 2023.

McNicholas C, Swor E, Wan L, Peipert JF. Prolonged use of the etonogestrel implant and levonorgestrel intrauterine device: 2 years beyond food and drug administration-approved duration. Am J Obstet Gynecol. 2017;216(6):586e.

Article   Google Scholar  

McNicholas C, Maddipati R, Zhao Q, Swor E, Peipert JF. Use of the etonogestrel implant and levonorgestrel intrauterine device beyond the U.S. food and drug administration-approved duration. Obstet Gynecol. 2015;125(3):599–604. https://doi.org/10.1097/AOG.0000000000000690 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ali M, Akin A, Bahamondes L, Brache V, Habib N, Landoulsi S, Hubacher D, WHO study group on subdermal contraceptive implants for women. Extended use up to 5 years of the etonogestrel-releasing subdermal contraceptive implant: comparison to levonorgestrel-releasing subdermal implant. Hum Reprod. 2016;31(11):2491–8. https://doi.org/10.1093/humrep/dew222 .

U.S. Food and Drug Administration. (1998). Off-label and investigational use of marketed drugs, biologics, and medical devices: guidance for institutional review boards and clinical investigators . Retrieved from https://www.fda.gov/regulatory-information/search-fda-guidance-documents/label-and-investigational-use-marketed-drugs-biologics-and-medical-devices . Accessed 20 Dec 2022.

Jensen JT, Lukkari-Lax E, Schulze A, Wahdan Y, Serrani M, Kroll R. Contraceptive efficacy and safety of the 52-mg levonorgestrel intrauterine system for up to 8 years: findings from the mirena extension trial. Am J Obstet Gynecol. 2022;227(6):873. https://doi.org/10.1016/j.ajog.2022.09.007 .

Article   CAS   Google Scholar  

O’Dwyer MC. Contraceptive Efficacy of the Mirena Intrauterine System Through 8 Years of Use. NEJM Journal Watch. Retrieved from https://www.jwatch.org/na55371/2022/10/04/contraceptive-efficacy-mirena-intrauterine-system-through . Accessed 7 Mar 2024.

Teunissen AM, Grimm B, Roumen FJ. Continuation rates of the subdermal contraceptive Implanon(®) and associated influencing factors. Eur J Contracept Reprod Health Care. 2014;19(1):15–21. https://doi.org/10.3109/13625187.2013.862231 .

Article   CAS   PubMed   Google Scholar  

Moray KV, Chaurasia H, Sachin O, Joshi B. A systematic review on clinical effectiveness, side-effect profile and meta-analysis on continuation rate of etonogestrel contraceptive implant. Reprod Health. 2021;18(1):4. https://doi.org/10.1186/s12978-020-01054-y .

Article   PubMed   PubMed Central   Google Scholar  

Blumenthal PD, Gemzell-Danielsson K, Marintcheva-Petrova M. Tolerability and clinical safety of implanon. Eur J Contracept Reprod Health Care. 2008;13(Suppl 1):29–36. https://doi.org/10.1080/13625180801960012 .

Mansour D, Korver T, Marintcheva-Petrova M, Fraser IS. The effects of implanon on menstrual bleeding patterns. Eur J Contracept Reprod Health Care. 2008;13(Suppl 1):13–28. https://doi.org/10.1080/13625180801959931 .

Diedrich JT, Zhao Q, Madden T, Secura GM, Peipert JF. Three-year continuation of reversible contraception. Am J Obstet Gynecol. 2015;213(5):e6621-6628. https://doi.org/10.1016/j.ajog.2015.08.001 .

Funk S, Miller MM, Mishell DR Jr, Archer DF, Poindexter A, Schmidt J, Zampaglione E, Implanon US Study Group. Safety and efficacy of implanon, a single-rod implantable contraceptive containing etonogestrel. Contraception. 2005;71(5):319–26. https://doi.org/10.1016/j.contraception.2004.11.007 .

Rigler N, Averbach S, Sandoval S, Meurice M, Hildebrand M, Mody SK. Barriers and facilitators of extended use of the contraceptive arm implants: a cross-sectional survey of clinicians. Obstet Gynecol. 2022;139:4S. https://doi.org/10.1097/01.AOG.0000826380.60071.b8 .

Hubacher D, Spector H, Monteith C, Chen PL. Not seeking yet trying long-acting reversible contraception: a 24-month randomized trial on continuation, unintended pregnancy and satisfaction. Contraception. 2018;97(6):524–32. https://doi.org/10.1016/j.contraception.2018.02.001 .

Winner B, Peipert JF, Zhao Q, Buckel C, Madden T, Allsworth JE, Secura GM. Effectiveness of long-acting reversible contraception. N Engl J Med. 2012;366(21):1998–2007. https://doi.org/10.1056/NEJMoa1110855 .

Monea E, Thomas A. Unintended pregnancy and taxpayer spending. Perspect Sex Reprod Health. 2011;43(2):88–93. https://doi.org/10.1363/4308811 .

Article   PubMed   Google Scholar  

CFIR Research Team-Center for Clinical Management Research. The Consolidated Framework for Implementation Research: Constructs. 2023. https://cfirguide.org/constructs/ . Accessed 24 Jan 2023.

Forman J, Damschroder LJ. Qualitative content analysis. In: Jacoby L, Siminoff L, editors. Empirical research for bioethics: A primer, vol. 11. Oxford: Elsevier Publishing; 2008. p. 39–62.

Chapter   Google Scholar  

Hill CE, Knox S, Thompson BJ, Williams EN, Hess SA. Consensual qualitative research: an update. J Couns Psychol. 2005;52:1–25.

Soratto J, Pires DEP, Friese S. Thematic content analysis using ATLAS.ti software: potentialities for researchs in health. Rev Bras Enferm. 2020;73(3):e20190250. https://doi.org/10.1590/0034-7167-2019-0250 .

Dethier D, Qasba N, Kaneshiro B. Society of family planning clinical recommendation: extended use of long-acting reversible contraception. Contraception. 2022;113:13–8. https://doi.org/10.1016/j.contraception.2022.06.003 .

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Acknowledgements

We thank the participants in this study.

This study was funded by Organon (Study #201908). The funder had no role in the study design, analysis, or interpretation of findings.

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School of Medicine, University of California San Diego, San Diego, CA, USA

Nicole Rigler

Division of Complex Family Planning, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, 9300 Campus Point Dr. MC 7433, La Jolla, San Diego, CA, USA

Gennifer Kully, Marisa C. Hildebrand, Sarah Averbach & Sheila K. Mody

Center on Gender Equity and Health, University of California, San Diego, CA, USA

Gennifer Kully & Sarah Averbach

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Contributions

SM is the principal investigator and lead data analysis, including qualitative coding, and dissemination of findings. She was also involved in study design and participant recruitment. NR was the primary interviewer and was involved in study design, recruitment, data management, data analysis, and dissemination of findings. GK and MH were involved with study design, recruitment, coordination of the study, IRB documentation, data analysis, and dissemination of findings. SA was involved with study design and dissemination of findings. All authors read and approved the final draft of the manuscript.

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Correspondence to Sheila K. Mody .

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This study was approved by the Institutional Review Board at University of California, San Diego (Study #201908). All participants gave written informed consent.

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Competing interests

S.M. is a consultant for Bayer and Merck. She has grant funding from Organon and receives authorship royalties from UpToDate. S.A. has served as a consultant for Bayer on immediate postpartum IUD use. The remaining authors report no conflict of interest.

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Rigler, N., Kully, G., Hildebrand, M.C. et al. Offering extended use of the contraceptive implant via an implementation science framework: a qualitative study of clinicians’ perceived barriers and facilitators. BMC Health Serv Res 24 , 697 (2024). https://doi.org/10.1186/s12913-024-10991-4

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Received : 19 December 2023

Accepted : 15 April 2024

Published : 03 June 2024

DOI : https://doi.org/10.1186/s12913-024-10991-4

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