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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

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The purpose of the discussion section is to interpret and describe the significance of your findings in relation to what was already known about the research problem being investigated and to explain any new understanding or insights that emerged as a result of your research. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but the discussion does not simply repeat or rearrange the first parts of your paper; the discussion clearly explains how your study advanced the reader's understanding of the research problem from where you left them at the end of your review of prior research.

Annesley, Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Peacock, Matthew. “Communicative Moves in the Discussion Section of Research Articles.” System 30 (December 2002): 479-497.

Importance of a Good Discussion

The discussion section is often considered the most important part of your research paper because it:

• Most effectively demonstrates your ability as a researcher to think critically about an issue, to develop creative solutions to problems based upon a logical synthesis of the findings, and to formulate a deeper, more profound understanding of the research problem under investigation;
• Presents the underlying meaning of your research, notes possible implications in other areas of study, and explores possible improvements that can be made in order to further develop the concerns of your research;
• Highlights the importance of your study and how it can contribute to understanding the research problem within the field of study;
• Presents how the findings from your study revealed and helped fill gaps in the literature that had not been previously exposed or adequately described; and,
• Engages the reader in thinking critically about issues based on an evidence-based interpretation of findings; it is not governed strictly by objective reporting of information.

Annesley Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Bitchener, John and Helen Basturkmen. “Perceptions of the Difficulties of Postgraduate L2 Thesis Students Writing the Discussion Section.” Journal of English for Academic Purposes 5 (January 2006): 4-18; Kretchmer, Paul. Fourteen Steps to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008.

Structure and Writing Style

I.  General Rules

These are the general rules you should adopt when composing your discussion of the results :

• Do not be verbose or repetitive; be concise and make your points clearly
• Avoid the use of jargon or undefined technical language
• Follow a logical stream of thought; in general, interpret and discuss the significance of your findings in the same sequence you described them in your results section [a notable exception is to begin by highlighting an unexpected result or a finding that can grab the reader's attention]
• Use the present verb tense, especially for established facts; however, refer to specific works or prior studies in the past tense
• If needed, use subheadings to help organize your discussion or to categorize your interpretations into themes

II.  The Content

The content of the discussion section of your paper most often includes :

• Explanation of results : Comment on whether or not the results were expected for each set of findings; go into greater depth to explain findings that were unexpected or especially profound. If appropriate, note any unusual or unanticipated patterns or trends that emerged from your results and explain their meaning in relation to the research problem.
• References to previous research : Either compare your results with the findings from other studies or use the studies to support a claim. This can include re-visiting key sources already cited in your literature review section, or, save them to cite later in the discussion section if they are more important to compare with your results instead of being a part of the general literature review of prior research used to provide context and background information. Note that you can make this decision to highlight specific studies after you have begun writing the discussion section.
• Deduction : A claim for how the results can be applied more generally. For example, describing lessons learned, proposing recommendations that can help improve a situation, or highlighting best practices.
• Hypothesis : A more general claim or possible conclusion arising from the results [which may be proved or disproved in subsequent research]. This can be framed as new research questions that emerged as a consequence of your analysis.

III.  Organization and Structure

Keep the following sequential points in mind as you organize and write the discussion section of your paper:

• Think of your discussion as an inverted pyramid. Organize the discussion from the general to the specific, linking your findings to the literature, then to theory, then to practice [if appropriate].
• Use the same key terms, narrative style, and verb tense [present] that you used when describing the research problem in your introduction.
• Begin by briefly re-stating the research problem you were investigating and answer all of the research questions underpinning the problem that you posed in the introduction.
• Describe the patterns, principles, and relationships shown by each major findings and place them in proper perspective. The sequence of this information is important; first state the answer, then the relevant results, then cite the work of others. If appropriate, refer the reader to a figure or table to help enhance the interpretation of the data [either within the text or as an appendix].
• Regardless of where it's mentioned, a good discussion section includes analysis of any unexpected findings. This part of the discussion should begin with a description of the unanticipated finding, followed by a brief interpretation as to why you believe it appeared and, if necessary, its possible significance in relation to the overall study. If more than one unexpected finding emerged during the study, describe each of them in the order they appeared as you gathered or analyzed the data. As noted, the exception to discussing findings in the same order you described them in the results section would be to begin by highlighting the implications of a particularly unexpected or significant finding that emerged from the study, followed by a discussion of the remaining findings.
• Before concluding the discussion, identify potential limitations and weaknesses if you do not plan to do so in the conclusion of the paper. Comment on their relative importance in relation to your overall interpretation of the results and, if necessary, note how they may affect the validity of your findings. Avoid using an apologetic tone; however, be honest and self-critical [e.g., in retrospect, had you included a particular question in a survey instrument, additional data could have been revealed].
• The discussion section should end with a concise summary of the principal implications of the findings regardless of their significance. Give a brief explanation about why you believe the findings and conclusions of your study are important and how they support broader knowledge or understanding of the research problem. This can be followed by any recommendations for further research. However, do not offer recommendations which could have been easily addressed within the study. This would demonstrate to the reader that you have inadequately examined and interpreted the data.

IV.  Overall Objectives

The objectives of your discussion section should include the following: I.  Reiterate the Research Problem/State the Major Findings

Briefly reiterate the research problem or problems you are investigating and the methods you used to investigate them, then move quickly to describe the major findings of the study. You should write a direct, declarative, and succinct proclamation of the study results, usually in one paragraph.

II.  Explain the Meaning of the Findings and Why They are Important

No one has thought as long and hard about your study as you have. Systematically explain the underlying meaning of your findings and state why you believe they are significant. After reading the discussion section, you want the reader to think critically about the results and why they are important. You don’t want to force the reader to go through the paper multiple times to figure out what it all means. If applicable, begin this part of the section by repeating what you consider to be your most significant or unanticipated finding first, then systematically review each finding. Otherwise, follow the general order you reported the findings presented in the results section.

III.  Relate the Findings to Similar Studies

No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your results to those found in other studies, particularly if questions raised from prior studies served as the motivation for your research. This is important because comparing and contrasting the findings of other studies helps to support the overall importance of your results and it highlights how and in what ways your study differs from other research about the topic. Note that any significant or unanticipated finding is often because there was no prior research to indicate the finding could occur. If there is prior research to indicate this, you need to explain why it was significant or unanticipated. IV.  Consider Alternative Explanations of the Findings

It is important to remember that the purpose of research in the social sciences is to discover and not to prove . When writing the discussion section, you should carefully consider all possible explanations for the study results, rather than just those that fit your hypothesis or prior assumptions and biases. This is especially important when describing the discovery of significant or unanticipated findings.

V.  Acknowledge the Study’s Limitations

It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor! Note any unanswered questions or issues your study could not address and describe the generalizability of your results to other situations. If a limitation is applicable to the method chosen to gather information, then describe in detail the problems you encountered and why. VI.  Make Suggestions for Further Research

You may choose to conclude the discussion section by making suggestions for further research [as opposed to offering suggestions in the conclusion of your paper]. Although your study can offer important insights about the research problem, this is where you can address other questions related to the problem that remain unanswered or highlight hidden issues that were revealed as a result of conducting your research. You should frame your suggestions by linking the need for further research to the limitations of your study [e.g., in future studies, the survey instrument should include more questions that ask..."] or linking to critical issues revealed from the data that were not considered initially in your research.

NOTE: Besides the literature review section, the preponderance of references to sources is usually found in the discussion section . A few historical references may be helpful for perspective, but most of the references should be relatively recent and included to aid in the interpretation of your results, to support the significance of a finding, and/or to place a finding within a particular context. If a study that you cited does not support your findings, don't ignore it--clearly explain why your research findings differ from theirs.

V.  Problems to Avoid

• Do not waste time restating your results . Should you need to remind the reader of a finding to be discussed, use "bridge sentences" that relate the result to the interpretation. An example would be: “In the case of determining available housing to single women with children in rural areas of Texas, the findings suggest that access to good schools is important...," then move on to further explaining this finding and its implications.
• As noted, recommendations for further research can be included in either the discussion or conclusion of your paper, but do not repeat your recommendations in the both sections. Think about the overall narrative flow of your paper to determine where best to locate this information. However, if your findings raise a lot of new questions or issues, consider including suggestions for further research in the discussion section.
• Do not introduce new results in the discussion section. Be wary of mistaking the reiteration of a specific finding for an interpretation because it may confuse the reader. The description of findings [results section] and the interpretation of their significance [discussion section] should be distinct parts of your paper. If you choose to combine the results section and the discussion section into a single narrative, you must be clear in how you report the information discovered and your own interpretation of each finding. This approach is not recommended if you lack experience writing college-level research papers.
• Use of the first person pronoun is generally acceptable. Using first person singular pronouns can help emphasize a point or illustrate a contrasting finding. However, keep in mind that too much use of the first person can actually distract the reader from the main points [i.e., I know you're telling me this--just tell me!].

Analyzing vs. Summarizing. Department of English Writing Guide. George Mason University; Discussion. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Hess, Dean R. "How to Write an Effective Discussion." Respiratory Care 49 (October 2004); Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008; The Lab Report. University College Writing Centre. University of Toronto; Sauaia, A. et al. "The Anatomy of an Article: The Discussion Section: "How Does the Article I Read Today Change What I Will Recommend to my Patients Tomorrow?” The Journal of Trauma and Acute Care Surgery 74 (June 2013): 1599-1602; Research Limitations & Future Research . Lund Research Ltd., 2012; Summary: Using it Wisely. The Writing Center. University of North Carolina; Schafer, Mickey S. Writing the Discussion. Writing in Psychology course syllabus. University of Florida; Yellin, Linda L. A Sociology Writer's Guide . Boston, MA: Allyn and Bacon, 2009.

Writing Tip

Don’t Over-Interpret the Results!

Interpretation is a subjective exercise. As such, you should always approach the selection and interpretation of your findings introspectively and to think critically about the possibility of judgmental biases unintentionally entering into discussions about the significance of your work. With this in mind, be careful that you do not read more into the findings than can be supported by the evidence you have gathered. Remember that the data are the data: nothing more, nothing less.

MacCoun, Robert J. "Biases in the Interpretation and Use of Research Results." Annual Review of Psychology 49 (February 1998): 259-287; Ward, Paulet al, editors. The Oxford Handbook of Expertise . Oxford, UK: Oxford University Press, 2018.

Another Writing Tip

Don't Write Two Results Sections!

One of the most common mistakes that you can make when discussing the results of your study is to present a superficial interpretation of the findings that more or less re-states the results section of your paper. Obviously, you must refer to your results when discussing them, but focus on the interpretation of those results and their significance in relation to the research problem, not the data itself.

Azar, Beth. "Discussing Your Findings."  American Psychological Association gradPSYCH Magazine (January 2006).

Yet Another Writing Tip

Avoid Unwarranted Speculation!

The discussion section should remain focused on the findings of your study. For example, if the purpose of your research was to measure the impact of foreign aid on increasing access to education among disadvantaged children in Bangladesh, it would not be appropriate to speculate about how your findings might apply to populations in other countries without drawing from existing studies to support your claim or if analysis of other countries was not a part of your original research design. If you feel compelled to speculate, do so in the form of describing possible implications or explaining possible impacts. Be certain that you clearly identify your comments as speculation or as a suggestion for where further research is needed. Sometimes your professor will encourage you to expand your discussion of the results in this way, while others don’t care what your opinion is beyond your effort to interpret the data in relation to the research problem.

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• Manuscript Preparation

6 Steps to Write an Excellent Discussion in Your Manuscript

• 4 minute read

Table of Contents

The discussion section in scientific manuscripts might be the last few paragraphs, but its role goes far beyond wrapping up. It’s the part of an article where scientists talk about what they found and what it means, where raw data turns into meaningful insights. Therefore, discussion is a vital component of the article.  

An excellent discussion is well-organized. We bring to you authors a classic 6-step method for writing discussion sections, with examples to illustrate the functions and specific writing logic of each step. Take a look at how you can impress journal reviewers with a concise and focused discussion section!  

Discussion frame structure   

Conventionally, a discussion section has three parts: an introductory paragraph, a few intermediate paragraphs, and a conclusion¹.  Please follow the steps below:  

1.Introduction—mention gaps in previous research¹⁻ ²

Here, you orient the reader to your study. In the first paragraph, it is advisable to mention the research gap your paper addresses.  

Example: This study investigated the cognitive effects of a meat-only diet on adults. While earlier studies have explored the impact of a carnivorous diet on physical attributes and agility, they have not explicitly addressed its influence on cognitively intense tasks involving memory and reasoning.  

2. Summarizing key findings—let your data speak ¹⁻ ²

After you have laid out the context for your study, recapitulate some of its key findings. Also, highlight key data and evidence supporting these findings.  

Example: We found that risk-taking behavior among teenagers correlates with their tendency to invest in cryptocurrencies. Risk takers in this study, as measured by the Cambridge Gambling Task, tended to have an inordinately higher proportion of their savings invested as crypto coins.  

3. Interpreting results—compare with other papers¹⁻²    

Here, you must analyze and interpret any results concerning the research question or hypothesis. How do the key findings of your study help verify or disprove the hypothesis? What practical relevance does your discovery have?  

Example: Our study suggests that higher daily caffeine intake is not associated with poor performance in major sporting events. Athletes may benefit from the cardiovascular benefits of daily caffeine intake without adversely impacting performance.    

Remember, unlike the results section, the discussion ideally focuses on locating your findings in the larger body of existing research. Hence, compare your results with those of other peer-reviewed papers.  

Example: Although Miller et al. (2020) found evidence of such political bias in a multicultural population, our findings suggest that the bias is weak or virtually non-existent among politically active citizens.  

4. Addressing limitations—their potential impact on the results¹⁻²    

Discuss the potential impact of limitations on the results. Most studies have limitations, and it is crucial to acknowledge them in the intermediary paragraphs of the discussion section. Limitations may include low sample size, suspected interference or noise in data, low effect size, etc.  

Example: This study explored a comprehensive list of adverse effects associated with the novel drug ‘X’. However, long-term studies may be needed to confirm its safety, especially regarding major cardiac events.  

5. Implications for future research—how to explore further¹⁻²    

Locate areas of your research where more investigation is needed. Concluding paragraphs of the discussion can explain what research will likely confirm your results or identify knowledge gaps your study left unaddressed.  

Example: Our study demonstrates that roads paved with the plastic-infused compound ‘Y’ are more resilient than asphalt. Future studies may explore economically feasible ways of producing compound Y in bulk.  

6. Conclusion—summarize content¹⁻²    

A good way to wind up the discussion section is by revisiting the research question mentioned in your introduction. Sign off by expressing the main findings of your study.  

Example: Recent observations suggest that the fish ‘Z’ is moving upriver in many parts of the Amazon basin. Our findings provide conclusive evidence that this phenomenon is associated with rising sea levels and climate change, not due to elevated numbers of invasive predators.  

A rigorous and concise discussion section is one of the keys to achieving an excellent paper. It serves as a critical platform for researchers to interpret and connect their findings with the broader scientific context. By detailing the results, carefully comparing them with existing research, and explaining the limitations of this study, you can effectively help reviewers and readers understand the entire research article more comprehensively and deeply¹⁻² , thereby helping your manuscript to be successfully published and gain wider dissemination.  

In addition to keeping this writing guide, you can also use Elsevier Language Services to improve the quality of your paper more deeply and comprehensively. We have a professional editing team covering multiple disciplines. With our profound disciplinary background and rich polishing experience, we can significantly optimize all paper modules including the discussion, effectively improve the fluency and rigor of your articles, and make your scientific research results consistent, with its value reflected more clearly. We are always committed to ensuring the quality of papers according to the standards of top journals, improving the publishing efficiency of scientific researchers, and helping you on the road to academic success. Check us out here !  

Type in wordcount for Standard Total: USD EUR JPY Follow this link if your manuscript is longer than 12,000 words. Upload  

References:   

• Masic, I. (2018). How to write an efficient discussion? Medical Archives , 72(3), 306. https://doi.org/10.5455/medarh.2018.72.306-307  
• Şanlı, Ö., Erdem, S., & Tefik, T. (2014). How to write a discussion section? Urology Research & Practice , 39(1), 20–24. https://doi.org/10.5152/tud.2013.049  

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How to Write the Discussion Section of a Research Paper

The discussion section of a research paper analyzes and interprets the findings, provides context, compares them with previous studies, identifies limitations, and suggests future research directions.

Updated on September 15, 2023

Structure your discussion section right, and you’ll be cited more often while doing a greater service to the scientific community. So, what actually goes into the discussion section? And how do you write it?

The discussion section of your research paper is where you let the reader know how your study is positioned in the literature, what to take away from your paper, and how your work helps them. It can also include your conclusions and suggestions for future studies.

First, we’ll define all the parts of your discussion paper, and then look into how to write a strong, effective discussion section for your paper or manuscript.

Discussion section: what is it, what it does

The discussion section comes later in your paper, following the introduction, methods, and results. The discussion sets up your study’s conclusions. Its main goals are to present, interpret, and provide a context for your results.

What is it?

The discussion section provides an analysis and interpretation of the findings, compares them with previous studies, identifies limitations, and suggests future directions for research.

This section combines information from the preceding parts of your paper into a coherent story. By this point, the reader already knows why you did your study (introduction), how you did it (methods), and what happened (results). In the discussion, you’ll help the reader connect the ideas from these sections.

Why is it necessary?

The discussion provides context and interpretations for the results. It also answers the questions posed in the introduction. While the results section describes your findings, the discussion explains what they say. This is also where you can describe the impact or implications of your research.

Adds context for your results

Most research studies aim to answer a question, replicate a finding, or address limitations in the literature. These goals are first described in the introduction. However, in the discussion section, the author can refer back to them to explain how the study's objective was achieved. 

Shows what your results actually mean and real-world implications

The discussion can also describe the effect of your findings on research or practice. How are your results significant for readers, other researchers, or policymakers?

What to include in your discussion (in the correct order)

A complete and effective discussion section should at least touch on the points described below.

Summary of key findings

The discussion should begin with a brief factual summary of the results. Concisely overview the main results you obtained.

Begin with key findings with supporting evidence

Your results section described a list of findings, but what message do they send when you look at them all together?

Your findings were detailed in the results section, so there’s no need to repeat them here, but do provide at least a few highlights. This will help refresh the reader’s memory and help them focus on the big picture.

Read the first paragraph of the discussion section in this article (PDF) for an example of how to start this part of your paper. Notice how the authors break down their results and follow each description sentence with an explanation of why each finding is relevant. 

State clearly and concisely

Following a clear and direct writing style is especially important in the discussion section. After all, this is where you will make some of the most impactful points in your paper. While the results section often contains technical vocabulary, such as statistical terms, the discussion section lets you describe your findings more clearly. 

Interpretation of results

Once you’ve given your reader an overview of your results, you need to interpret those results. In other words, what do your results mean? Discuss the findings’ implications and significance in relation to your research question or hypothesis.

Analyze and interpret your findings

Look into your findings and explore what’s behind them or what may have caused them. If your introduction cited theories or studies that could explain your findings, use these sources as a basis to discuss your results.

For example, look at the second paragraph in the discussion section of this article on waggling honey bees. Here, the authors explore their results based on information from the literature.

Unexpected or contradictory results

Sometimes, your findings are not what you expect. Here’s where you describe this and try to find a reason for it. Could it be because of the method you used? Does it have something to do with the variables analyzed? Comparing your methods with those of other similar studies can help with this task.

Context and comparison with previous work

Refer to related studies to place your research in a larger context and the literature. Compare and contrast your findings with existing literature, highlighting similarities, differences, and/or contradictions.

How your work compares or contrasts with previous work

Studies with similar findings to yours can be cited to show the strength of your findings. Information from these studies can also be used to help explain your results. Differences between your findings and others in the literature can also be discussed here. 

How to divide this section into subsections

If you have more than one objective in your study or many key findings, you can dedicate a separate section to each of these. Here’s an example of this approach. You can see that the discussion section is divided into topics and even has a separate heading for each of them. 

Limitations

Many journals require you to include the limitations of your study in the discussion. Even if they don’t, there are good reasons to mention these in your paper.

Why limitations don’t have a negative connotation

A study’s limitations are points to be improved upon in future research. While some of these may be flaws in your method, many may be due to factors you couldn’t predict.

Examples include time constraints or small sample sizes. Pointing this out will help future researchers avoid or address these issues. This part of the discussion can also include any attempts you have made to reduce the impact of these limitations, as in this study .

How limitations add to a researcher's credibility

Pointing out the limitations of your study demonstrates transparency. It also shows that you know your methods well and can conduct a critical assessment of them.  

Implications and significance

The final paragraph of the discussion section should contain the take-home messages for your study. It can also cite the “strong points” of your study, to contrast with the limitations section.

Restate your hypothesis

Remind the reader what your hypothesis was before you conducted the study. 

How was it proven or disproven?

Identify your main findings and describe how they relate to your hypothesis.

How your results contribute to the literature

Were you able to answer your research question? Or address a gap in the literature?

Future implications of your research

Describe the impact that your results may have on the topic of study. Your results may show, for instance, that there are still limitations in the literature for future studies to address. There may be a need for studies that extend your findings in a specific way. You also may need additional research to corroborate your findings. 

Sample discussion section

This fictitious example covers all the aspects discussed above. Your actual discussion section will probably be much longer, but you can read this to get an idea of everything your discussion should cover.

Our results showed that the presence of cats in a household is associated with higher levels of perceived happiness by its human occupants. These findings support our hypothesis and demonstrate the association between pet ownership and well-being. 

The present findings align with those of Bao and Schreer (2016) and Hardie et al. (2023), who observed greater life satisfaction in pet owners relative to non-owners. Although the present study did not directly evaluate life satisfaction, this factor may explain the association between happiness and cat ownership observed in our sample.

Our findings must be interpreted in light of some limitations, such as the focus on cat ownership only rather than pets as a whole. This may limit the generalizability of our results.

Nevertheless, this study had several strengths. These include its strict exclusion criteria and use of a standardized assessment instrument to investigate the relationships between pets and owners. These attributes bolster the accuracy of our results and reduce the influence of confounding factors, increasing the strength of our conclusions. Future studies may examine the factors that mediate the association between pet ownership and happiness to better comprehend this phenomenon.

This brief discussion begins with a quick summary of the results and hypothesis. The next paragraph cites previous research and compares its findings to those of this study. Information from previous studies is also used to help interpret the findings. After discussing the results of the study, some limitations are pointed out. The paper also explains why these limitations may influence the interpretation of results. Then, final conclusions are drawn based on the study, and directions for future research are suggested.

How to make your discussion flow naturally

If you find writing in scientific English challenging, the discussion and conclusions are often the hardest parts of the paper to write. That’s because you’re not just listing up studies, methods, and outcomes. You’re actually expressing your thoughts and interpretations in words.

• How formal should it be?
• What words should you use, or not use?
• How do you meet strict word limits, or make it longer and more informative?

Always give it your best, but sometimes a helping hand can, well, help. Getting a professional edit can help clarify your work’s importance while improving the English used to explain it. When readers know the value of your work, they’ll cite it. We’ll assign your study to an expert editor knowledgeable in your area of research. Their work will clarify your discussion, helping it to tell your story. Find out more about AJE Editing.

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How to Write a Discussion Section for a Research Paper

We’ve talked about several useful writing tips that authors should consider while drafting or editing their research papers. In particular, we’ve focused on  figures and legends , as well as the Introduction ,  Methods , and  Results . Now that we’ve addressed the more technical portions of your journal manuscript, let’s turn to the analytical segments of your research article. In this article, we’ll provide tips on how to write a strong Discussion section that best portrays the significance of your research contributions.

What is the Discussion section of a research paper?

In a nutshell,  your Discussion fulfills the promise you made to readers in your Introduction . At the beginning of your paper, you tell us why we should care about your research. You then guide us through a series of intricate images and graphs that capture all the relevant data you collected during your research. We may be dazzled and impressed at first, but none of that matters if you deliver an anti-climactic conclusion in the Discussion section!

Are you feeling pressured? Don’t worry. To be honest, you will edit the Discussion section of your manuscript numerous times. After all, in as little as one to two paragraphs ( Nature ‘s suggestion  based on their 3,000-word main body text limit), you have to explain how your research moves us from point A (issues you raise in the Introduction) to point B (our new understanding of these matters). You must also recommend how we might get to point C (i.e., identify what you think is the next direction for research in this field). That’s a lot to say in two paragraphs!

So, how do you do that? Let’s take a closer look.

What should I include in the Discussion section?

As we stated above, the goal of your Discussion section is to  answer the questions you raise in your Introduction by using the results you collected during your research . The content you include in the Discussions segment should include the following information:

• Remind us why we should be interested in this research project.
• Describe the nature of the knowledge gap you were trying to fill using the results of your study.
• Don’t repeat your Introduction. Instead, focus on why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place.
• Mainly, you want to remind us of how your research will increase our knowledge base and inspire others to conduct further research.
• Clearly tell us what that piece of missing knowledge was.
• Answer each of the questions you asked in your Introduction and explain how your results support those conclusions.
• Make sure to factor in all results relevant to the questions (even if those results were not statistically significant).
• Focus on the significance of the most noteworthy results.
• If conflicting inferences can be drawn from your results, evaluate the merits of all of them.
• Don’t rehash what you said earlier in the Results section. Rather, discuss your findings in the context of answering your hypothesis. Instead of making statements like “[The first result] was this…,” say, “[The first result] suggests [conclusion].”
• Do your conclusions line up with existing literature?
• Discuss whether your findings agree with current knowledge and expectations.
• Keep in mind good persuasive argument skills, such as explaining the strengths of your arguments and highlighting the weaknesses of contrary opinions.
• If you discovered something unexpected, offer reasons. If your conclusions aren’t aligned with current literature, explain.
• Address any limitations of your study and how relevant they are to interpreting your results and validating your findings.
• Make sure to acknowledge any weaknesses in your conclusions and suggest room for further research concerning that aspect of your analysis.
• Make sure your suggestions aren’t ones that should have been conducted during your research! Doing so might raise questions about your initial research design and protocols.
• Similarly, maintain a critical but unapologetic tone. You want to instill confidence in your readers that you have thoroughly examined your results and have objectively assessed them in a way that would benefit the scientific community’s desire to expand our knowledge base.
• Recommend next steps.
• Your suggestions should inspire other researchers to conduct follow-up studies to build upon the knowledge you have shared with them.
• Keep the list short (no more than two).

How to Write the Discussion Section

The above list of what to include in the Discussion section gives an overall idea of what you need to focus on throughout the section. Below are some tips and general suggestions about the technical aspects of writing and organization that you might find useful as you draft or revise the contents we’ve outlined above.

Technical writing elements

• Embrace active voice because it eliminates the awkward phrasing and wordiness that accompanies passive voice.
• Use the present tense, which should also be employed in the Introduction.
• Sprinkle with first person pronouns if needed, but generally, avoid it. We want to focus on your findings.
• Maintain an objective and analytical tone.

Discussion section organization

• Keep the same flow across the Results, Methods, and Discussion sections.
• We develop a rhythm as we read and parallel structures facilitate our comprehension. When you organize information the same way in each of these related parts of your journal manuscript, we can quickly see how a certain result was interpreted and quickly verify the particular methods used to produce that result.
• Notice how using parallel structure will eliminate extra narration in the Discussion part since we can anticipate the flow of your ideas based on what we read in the Results segment. Reducing wordiness is important when you only have a few paragraphs to devote to the Discussion section!
• Within each subpart of a Discussion, the information should flow as follows: (A) conclusion first, (B) relevant results and how they relate to that conclusion and (C) relevant literature.
• End with a concise summary explaining the big-picture impact of your study on our understanding of the subject matter. At the beginning of your Discussion section, you stated why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place. Now, it is time to end with “how your research filled that gap.”

Discussion Part 1: Summarizing Key Findings

Begin the Discussion section by restating your  statement of the problem  and briefly summarizing the major results. Do not simply repeat your findings. Rather, try to create a concise statement of the main results that directly answer the central research question that you stated in the Introduction section . This content should not be longer than one paragraph in length.

Many researchers struggle with understanding the precise differences between a Discussion section and a Results section . The most important thing to remember here is that your Discussion section should subjectively evaluate the findings presented in the Results section, and in relatively the same order. Keep these sections distinct by making sure that you do not repeat the findings without providing an interpretation.

Phrase examples: Summarizing the results

• The findings indicate that …
• These results suggest a correlation between A and B …
• The data present here suggest that …
• An interpretation of the findings reveals a connection between…

Discussion Part 2: Interpreting the Findings

What do the results mean? It may seem obvious to you, but simply looking at the figures in the Results section will not necessarily convey to readers the importance of the findings in answering your research questions.

The exact structure of interpretations depends on the type of research being conducted. Here are some common approaches to interpreting data:

• Identifying correlations and relationships in the findings
• Explaining whether the results confirm or undermine your research hypothesis
• Giving the findings context within the history of similar research studies
• Discussing unexpected results and analyzing their significance to your study or general research
• Offering alternative explanations and arguing for your position

Organize the Discussion section around key arguments, themes, hypotheses, or research questions or problems. Again, make sure to follow the same order as you did in the Results section.

Discussion Part 3: Discussing the Implications

In addition to providing your own interpretations, show how your results fit into the wider scholarly literature you surveyed in the  literature review section. This section is called the implications of the study . Show where and how these results fit into existing knowledge, what additional insights they contribute, and any possible consequences that might arise from this knowledge, both in the specific research topic and in the wider scientific domain.

Questions to ask yourself when dealing with potential implications:

• Do your findings fall in line with existing theories, or do they challenge these theories or findings? What new information do they contribute to the literature, if any? How exactly do these findings impact or conflict with existing theories or models?
• What are the practical implications on actual subjects or demographics?
• What are the methodological implications for similar studies conducted either in the past or future?

Your purpose in giving the implications is to spell out exactly what your study has contributed and why researchers and other readers should be interested.

Phrase examples: Discussing the implications of the research

• These results confirm the existing evidence in X studies…
• The results are not in line with the foregoing theory that…
• This experiment provides new insights into the connection between…
• These findings present a more nuanced understanding of…
• While previous studies have focused on X, these results demonstrate that Y.

Step 4: Acknowledging the limitations

All research has study limitations of one sort or another. Acknowledging limitations in methodology or approach helps strengthen your credibility as a researcher. Study limitations are not simply a list of mistakes made in the study. Rather, limitations help provide a more detailed picture of what can or cannot be concluded from your findings. In essence, they help temper and qualify the study implications you listed previously.

Study limitations can relate to research design, specific methodological or material choices, or unexpected issues that emerged while you conducted the research. Mention only those limitations directly relate to your research questions, and explain what impact these limitations had on how your study was conducted and the validity of any interpretations.

Possible types of study limitations:

• Insufficient sample size for statistical measurements
• Lack of previous research studies on the topic
• Methods/instruments/techniques used to collect the data
• Limited access to data
• Time constraints in properly preparing and executing the study

After discussing the study limitations, you can also stress that your results are still valid. Give some specific reasons why the limitations do not necessarily handicap your study or narrow its scope.

Phrase examples: Limitations sentence beginners

• “There may be some possible limitations in this study.”
• “The findings of this study have to be seen in light of some limitations.”
•  “The first limitation is the…The second limitation concerns the…”
•  “The empirical results reported herein should be considered in the light of some limitations.”
• “This research, however, is subject to several limitations.”
• “The primary limitation to the generalization of these results is…”
• “Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”

Discussion Part 5: Giving Recommendations for Further Research

Based on your interpretation and discussion of the findings, your recommendations can include practical changes to the study or specific further research to be conducted to clarify the research questions. Recommendations are often listed in a separate Conclusion section , but often this is just the final paragraph of the Discussion section.

Suggestions for further research often stem directly from the limitations outlined. Rather than simply stating that “further research should be conducted,” provide concrete specifics for how future can help answer questions that your research could not.

Phrase examples: Recommendation sentence beginners

• Further research is needed to establish …
• There is abundant space for further progress in analyzing…
• A further study with more focus on X should be done to investigate…
• Further studies of X that account for these variables must be undertaken.

Consider Receiving Professional Language Editing

As you edit or draft your research manuscript, we hope that you implement these guidelines to produce a more effective Discussion section. And after completing your draft, don’t forget to submit your work to a professional proofreading and English editing service like Wordvice, including our manuscript editing service for  paper editing , cover letter editing , SOP editing , and personal statement proofreading services. Language editors not only proofread and correct errors in grammar, punctuation, mechanics, and formatting but also improve terms and revise phrases so they read more naturally. Wordvice is an industry leader in providing high-quality revision for all types of academic documents.

For additional information about how to write a strong research paper, make sure to check out our full  research writing series !

Wordvice Writing Resources

• How to Write a Research Paper Introduction 
• Which Verb Tenses to Use in a Research Paper
• How to Write an Abstract for a Research Paper
• How to Write a Research Paper Title
• Useful Phrases for Academic Writing
• Common Transition Terms in Academic Papers
• Active and Passive Voice in Research Papers
• 100+ Verbs That Will Make Your Research Writing Amazing
• Tips for Paraphrasing in Research Papers

Additional Academic Resources

•   Guide for Authors.  (Elsevier)
•  How to Write the Results Section of a Research Paper.  (Bates College)
•   Structure of a Research Paper.  (University of Minnesota Biomedical Library)
•   How to Choose a Target Journal  (Springer)
•   How to Write Figures and Tables  (UNC Writing Center)

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Writing a discussion section: how to integrate substantive and statistical expertise

Michael höfler.

1 Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany

5 Chair of Clinical Psychology and Behavioural Neuroscience, Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany

2 Behavioral Epidemiology, Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany

Sebastian Trautmann

Robert miller.

3 Faculty of Psychology, Technische Universität Dresden, Dresden, Germany

4 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden

Associated Data

Not applicable.

When discussing results medical research articles often tear substantive and statistical (methodical) contributions apart, just as if both were independent. Consequently, reasoning on bias tends to be vague, unclear and superficial. This can lead to over-generalized, too narrow and misleading conclusions, especially for causal research questions.

To get the best possible conclusion, substantive and statistical expertise have to be integrated on the basis of reasonable assumptions. While statistics should raise questions on the mechanisms that have presumably created the data, substantive knowledge should answer them. Building on the related principle of Bayesian thinking, we make seven specific and four general proposals on writing a discussion section.

Misinterpretation could be reduced if authors explicitly discussed what can be concluded under which assumptions. Informed on the resulting conditional conclusions other researchers may, according to their knowledge and beliefs, follow a particular conclusion or, based on other conditions, arrive at another one. This could foster both an improved debate and a better understanding of the mechanisms behind the data and should therefore enable researchers to better address bias in future studies.

After a research article has presented the substantive background, the methods and the results, the discussion section assesses the validity of results and draws conclusions by interpreting them. The discussion puts the results into a broader context and reflects their implications for theoretical (e.g. etiological) and practical (e.g. interventional) purposes. As such, the discussion contains an article’s last words the reader is left with.

Common recommendations for the discussion section include general proposals for writing [ 1 ] and structuring (e.g. with a paragraph on a study’s strengths and weaknesses) [ 2 ], to avoid common statistical pitfalls (like misinterpreting non-significant findings as true null results) [ 3 ] and to “go beyond the data” when interpreting results [ 4 ]. Note that the latter includes much more than comparing an article’s results with the literature. If results and literature are consistent, this might be due to shared bias only. If they are not consistent, the question arises why inconsistency occurs – maybe because of bias acting differently across studies [ 5 – 7 ]. Recommendations like the CONSORT checklist do well in demanding all quantitative information on design, participation, compliance etc. to be reported in the methods and results section and “addressing sources of potential bias”, “limitations” and “considering other relevant evidence” in the discussion [ 8 , 9 ]. Similarly, the STROBE checklist for epidemiological research demands “a cautious overall interpretation of results” and "discussing the generalizability (external validity)" [ 10 , 11 ]. However, these guidelines do not clarify how to deal with the complex bias issue, and how to get to and report conclusions.

Consequently, suggestions on writing a discussion often remain vague by hardly addressing the role of the assumptions that have (often implicitly) been made when designing a study, analyzing the data and interpreting the results. Such assumptions involve mechanisms that have created the data and are related to sampling, measurement and treatment assignment (in observational studies common causes of factor and outcome) and, as a consequence, the bias this may produce [ 5 , 6 ]. They determine whether a result allows only an associational or a causal conclusion. Causal conclusions, if true, are of much higher relevance for etiology, prevention and intervention. However, they require much stronger assumptions. These have to be fully explicit and, therewith, essential part of the debate since they always involve subjectivity. Subjectivity is unavoidable because the mechanisms behind the data can never be fully estimated from the data themselves [ 12 ].

In this article, we argue that the conjunction of substantive and statistical (methodical) knowledge in the verbal integration of results and beliefs on mechanisms can be greatly improved in (medical) research papers. We illustrate this through the personal roles that a statistician (i.e. methods expert) and a substantive researcher should take. Doing so, we neither claim that usually just two people write a discussion, nor that one person lacks the knowledge of the other, nor that there were truly no researchers that have both kinds of expertise. As a metaphor, the division of these two roles into two persons describes the necessary integration of knowledge via the mode of a dialogue. Verbally, it addresses the finding of increased specialization of different study contributors in biomedical research. This has teared apart the two processes of statistical compilation of results and their verbal integration [ 13 ]. When this happens a statistician alone is limited to a study’s conditions (sampled population, experimental settings etc.), because he or she is unaware of the conditions’ generalizability. On the other hand, a A substantive expert alone is prone to over-generalize because he or she is not aware of the (mathematical) prerequisites for an interpretation.

The article addresses both (medical) researchers educated in basic statistics and research methods and statisticians who cooperate with them. Throughout the paper we exemplify our arguments with the finding of an association in a cross-tabulation between a binary X (factor) and a binary Y (outcome): those who are exposed to or treated with X have a statistically significantly elevated risk for Y as compared to the non-exposed or not (or otherwise) treated (for instance via the chi-squared independence test or logistic regression). Findings like this are frequent and raise the question which more profound conclusion is valid under what assumptions. Until some decades ago, statistics has largely avoided the related topic of causality and instead limited itself on describing observed distributions (here a two-by-two table between D = depression and LC = lung cancer) with well-fitting models.

We illustrate our arguments with the concrete example of the association found between the factor depression (D) and the outcome lung cancer (LC) [ 14 ]. Yet very different mechanisms could have produced such an association [ 7 ], and assumptions on these lead to the following fundamentally different conclusions (Fig. ​ (Fig.1 1 ):

• D causes LC (e.g. because smoking might constitute “self-medication” of depression symptoms)
• LC causes D (e.g. because LC patients are demoralized by their diagnosis)
• D and LC cause each other (e.g. because the arguments in both a. and b. apply)
• D and LC are the causal consequence of the same factor(s) (e.g. poor health behaviors - HB)
• D and LC only share measurement error (e.g. because a fraction of individuals that has either depression or lung cancer denies both in self-report measures).

Different conclusions about an association between D and LC. a D causes LC, b LC causes B, c D and LC cause each other, d D and LC are associated because of a shared factor (HB), e D and LC are associated because they have correlated errors

Note that we use the example purely for illustrative purposes. We do not make substantive claims on what of a. through e. is true but show how one should reflect on mechanisms in order to find the right answer. Besides, we do not consider research on the D-LC relation apart from the finding of association [ 14 ].

Assessing which of a. through e. truly applies requires substantive assumptions on mechanisms: the temporal order of D and LC (a causal effect requires that the cause occurs before the effect), shared factors, selection processes and measurement error. Questions on related mechanisms have to be brought up by statistical consideration, while substantive reasoning has to address them. Together this yields provisional assumptions for inferring that are subject to readers’ substantive consideration and refinement. In general, the integration of prior beliefs (anything beyond the data a conclusion depends on) and the results from the data themselves is formalized by Bayesian statistics [ 15 , 16 ]. This is beyond the scope of this article, still we argue that Bayesian thinking should govern the process of drawing conclusions.

Building on this idea, we provide seven specific and four general recommendations for the cooperative process of writing a discussion. The recommendations are intended to be suggestions rather than rules. They should be subject to further refinement and adjustment to specific requirements in different fields of medical and other research. Note that the order of the points is not meant to structure a discussion’s writing (besides 1.).

Recommendations for writing a discussion section

Specific recommendations.

Consider the example on the association between D and LC. Rather than starting with an in-depth (causal) interpretation a finding should firstly be taken as what it allows inferring without doubt: Under the usual assumptions that a statistical model makes (e.g. random sampling, independence or certain correlation structure between observations [ 17 ]), the association indicates that D (strictly speaking: measuring D) predicts an elevated LC risk (strictly speaking: measuring LC) in the population that one has managed to sample (source population). Assume that the sample has been randomly drawn from primary care settings. In this case the association is useful to recommend medical doctors to better look at an individual’s LC risk in case of D. If the association has been adjusted for age and gender (conveniently through a regression model), the conclusion modifies to: If the doctor knows a patient’s age and gender (what should always be the case) D has additional value in predicting an elevated LC risk.

In the above example, a substantive researcher might want to conclude that D and LC are associated in a general population instead of just inferring to patients in primary care settings (a.). Another researcher might even take the finding as evidence for D being a causal factor in the etiology of LC, meaning that prevention of D could reduce the incidence rate of LC (in whatever target population) (b.). In both cases, the substantive researcher should insist on assessing the desired interpretation that goes beyond the data [ 4 ], but the statistician immediately needs to bring up the next point.

The explanation of all the assumptions that lead from a data result to a conclusion enables a reader to assess whether he or she agrees with the authors’ inference or not. These conditions, however, often remain incomplete or unclear, in which case the reader can hardly assess whether he or she follows a path of argumentation and, thus, shares the conclusion this path leads to.

Consider conclusion a. and suppose that, instead of representative sampling in a general population (e.g. all U.S. citizens aged 18 or above), the investigators were only able to sample in primary care settings. Extrapolating the results to another population than the source population requires what is called “external validity”, “transportability” or the absence of “selection bias” [ 18 , 19 ]. No such bias occurs if the parameter of interest is equal in the source and the target population. Note that this is a weaker condition than the common belief that the sample must represent the target population in everything . If the parameter of interest is the difference in risk for LC between cases and non-cases of D, the condition translates into: the risk difference must be equal in target and source population.

For the causal conclusion b., however, sufficient assumptions are very strict. In an RCT, the conclusion is valid under random sampling from the target population, random allocation of X, perfect compliance in X, complete participation and no measurement error in outcome (for details see [ 20 ]). In practice, on the other hand, the derivations from such conditions might sometimes be modest what may produce little bias only. For instance, non-compliance in a specific drug intake (treatment) might occur only in a few individuals to little extent through a random process (e.g. sickness of a nurse being responsible for drug dispense) and yield just small (downward) bias [ 5 ]. The conclusion of downward bias might also be justified if non-compliance does not cause anything that has a larger effect on a Y than the drug itself. Another researcher, however, could believe that non-compliance leads to taking a more effective, alternative treatment. He or she could infer upward bias instead if well-informed on the line of argument.

In practice, researchers frequently use causal language yet without mentioning any assumptions. This does not imply that they truly have a causal effect in mind, often causal and associational wordings are carelessly used in synonymous way. For example, concluding “depression increases the risk of lung cancer” constitutes already causal wording because it implies that a change in the depression status would change the cancer risk. Associational language like “lung cancer risk is elevated if depression occurs”, however, would allow for an elevated lung cancer risk in depression cases just because LC and D share some causes (“inducing” or “removing” depression would not change the cancer risk here).

Often, it is unclear where the path of argumentation from assumptions to a conclusion leads when alternative assumptions are made. Consider again bias due to selection. A different effect in target and source population occurs if effect-modifying variables distribute differently in both populations. Accordingly, the statistician should ask which variables influence the effect of interest, and whether these can be assumed to distribute equally in the source population and the target population. The substantive researcher might answer that the causal risk difference between D and LC likely increases with age. Given that this is true, and if elder individuals have been oversampled (e.g. because elderly are over-represented in primary care settings), both together would conclude that sampling has led to over-estimation (despite other factors, Fig. ​ Fig.2 2 ).

If higher age is related to a larger effect (risk difference) of D on LC, a larger effect estimate is expected in an elder sample

However, the statistician might add, if effect modification is weak, or the difference in the age distributions is modest (e.g. mean 54 vs. 52 years), selection is unlikely to have produced large (here: upward) bias. In turn, another substantive researcher, who reads the resulting discussion, might instead assume a decrease of effect with increasing age and thus infer downward bias.

In practice, researchers should be extremely sensitive for bias due to selection if a sample has been drawn conditionally on a common consequence of factor and outcome or a variable associated with such a consequence [19 and references therein]. For instance, hospitalization might be influenced by both D and LC, and thus sampling from hospitals might introduce a false association or change an association’s sign; particularly D and LC may appear to be negatively associated although the association is positive in the general population (Fig. ​ (Fig.3 3 ).

If hospitalization (H) is a common cause of D and LC, sampling conditionally on H can introduce a spurious association between D and LC ("conditioning on a collider")

Usually, only some kinds of bias are discussed, while the consequences of others are ignored [ 5 ]. Besides selection the main sources of bias are often measurement and confounding. If one is only interested in association, confounding is irrelevant. For causal conclusions, however, assumptions on all three kinds of bias are necessary.

Measurement error means that the measurement of a factor and/or outcome deviates from the true value, at least in some individuals. Bias due to measurement is known under many other terms that describe the reasons why such error occurs (e.g. “recall bias” and “reporting bias”). In contrast to conventional wisdom, measurement error does not always bias association and effect estimates downwards [ 5 , 6 ]. It does, for instance, if only the factor (e.g. depression) is measured with error and the errors occur independently from the outcome (e.g. lung cancer), or vice versa (“non-differential misclassification”) [22 and references therein]. However, many lung cancer cases might falsely report depression symptoms (e.g. to express need for care). Such false positives (non-cases of depression classified as cases) may also occur in non-cases of lung cancer but to a lesser extent (a special case of “differential misclassification”). Here, bias might be upward as well. Importantly, false positives cause larger bias than false negatives (non-cases of depression falsely classified as depression cases) as long as the relative frequency of a factor is lower than 50% [ 21 ]. Therefore, they should receive more attention in discussion. If measurement error occurs in depression and lung cancer, the direction of bias also depends on the correlation between both errors [ 21 ].

Note that what is in line with common standards of “good” measurement (e.g. a Kappa value measuring validity or reliability of 0.7) might anyway produce large bias. This applies to estimates of prevalence, association and effect. The reason is that while indices of measurement are one-dimensional, bias depends on two parameters (sensitivity and specificity) [ 21 , 22 ]. Moreover, estimates of such indices are often extrapolated to different kinds of populations (typically from a clinical to general population), what may be inadequate. Note that the different kinds of bias often interact, e.g. bias due to measurement might depend on selection (e.g. measurement error might differ between a clinical and a general population) [ 5 , 6 ].

Assessment of bias due to confounding variables (roughly speaking: common causes of factor and outcome) requires assumptions on the entire system of variables that affect both factor and outcome. For example, D and LC might share several causes such as stressful life events or socioeconomic status. If these influence D and LC with the same effect direction, this leads to overestimation, otherwise (different effect directions) the causal effect is underestimated. In the medical field, many unfavorable conditions may be positively related. If this holds true for all common factors of D and LC, upward bias can be assumed. However, not all confounders have to be taken into account. Within the framework of “causal graphs”, the “backdoor criterion” [ 7 ] provides a graphical rule for sets of confounders to be sufficient when adjusted for. Practically, such a causal graph must include all factors that directly or indirectly affect both D and LC. Then, adjustment for a set of confounders that meets the “backdoor criterion” in the graph completely removes bias due to confounding. In the example of Fig. ​ Fig.4 4 it is sufficient to adjust for Z 1 and Z 2 because this “blocks” all paths that otherwise lead backwards from D to LC. Note that fully eliminating bias due to confounding also requires that the confounders have been collected without measurement error [ 5 , 6 , 23 ]. Therefore, the advice is always to concede at least some “residual” bias and reflect on the direction this might have (could be downward if such error is not stronger related to D and LC than a confounder itself).

Causal graph for the effect of D on LC and confounders Z 1 , Z 2 and Z 3

Whereas the statistician should pinpoint to the mathematical insight of the backdoor criterion, its application requires profound substantive input and literature review. Of course, there are numerous relevant factors in the medical field. Hence, one should practically focus on those with the highest prevalence (a very seldom factor can hardly cause large bias) and large assumed effects on both X and Y.

If knowledge on any of the three kinds of bias is poor or very uncertain, researchers should admit that this adds uncertainty in a conclusion: systematic error on top of random error. In the Bayesian framework, quantitative bias analysis formalizes this through the result of larger variance in an estimate. Technically, this additional variance is introduced via the variances of distributions assigned to “bias parameters”; for instance a misclassification probability (e.g. classifying a true depression case as non-case) or the prevalence of a binary confounder and its effects on X and Y. Of course, bias analysis also changes point estimates (hopefully reducing bias considerably). Note that conventional frequentist analysis, as regarded from the Bayesian perspective, assumes that all bias parameters were zero with a probability of one [ 5 , 6 , 23 ]. The only exceptions (bias addressed in conventional analyses) are adjustment on variables to hopefully reduce bias due to confounding and weighting the individuals (according to variables related to participation) to take into account bias due to selection.

If the substantive investigator understands the processes of selection, measurement and confounding only poorly, such strict analysis numerically reveals that little to nothing is known on the effect of X on Y, no matter how large an observed association and a sample (providing small random error) may be [ 5 , 6 , 23 ]). This insight has to be brought up by the statistician. Although such an analysis is complicated, itself very sensitive to how it is conducted [ 5 , 6 ] and rarely done, the Bayesian thinking behind it forces researchers to better understand the processes behind the data. Otherwise, he or she cannot make any assumptions and, in turn, no conclusion on causality.

Usually articles end with statements that only go little further than the always true but never informative statement “more research is needed”. Moreover, larger samples and better measurements are frequently proposed. If an association has been found, a RCT or other interventional study is usually proposed to investigate causality. In our example, this recommendation disregards that: (1) onset of D might have a different effect on LC risk than an intervention against D (the effect of onset cannot be investigated in any interventional study), (2) the effects of onset and intervention concern different populations (those without vs. those with depression), (3) an intervention effect depends on the mode of intervention [ 24 ], and (4) (applying the backdoor criterion) a well-designed observational study may approximatively yield the same result as a randomized study would [ 25 – 27 ]. If the effect of “removing” depression is actually of interest, one could propose an RCT that investigates the effect of treating depression in a strictly defined way and in a strictly defined population (desirably in all who meet the criteria of depression). Ideally, this population is sampled randomly, and non-participants and dropouts are investigated with respect to assumed effect-modifiers (differences in their distributions between participants and non-participants can then be addressed e.g. by weighting [ 27 ]). In a non-randomized study, one should collect variables supposed to meet the backdoor-criterion with the best instruments possible.

General recommendations

Yet when considering 1) through 7); i.e. carefully reflecting on the mechanisms that have created the data, discussions on statistical results can be very misleading, because the basic statistical methods are mis-interpreted or inadequately worded.

A common pitfall is to consider the lack of evidence for the alternative hypothesis (e.g. association between D and LC) as evidence for the null hypothesis (no association). In fact, such inference requires an a-priori calculated sample-size to ensure that the type-two error probability does not exceed a pre-specified limit (typically 20% or 10%, given the other necessary assumptions, e.g. on the true magnitude of association). Otherwise, the type-two error is unknown and in practice often large. This may put a “false negative result” into the scientific public that turns out to be “unreplicable” – what would be falsely interpreted as part of the “replication crisis”. Such results are neither positive nor negative but uninformative . In this case, the wording “there is no evidence for an association” is adequate because it does not claim that there is no association.

Frequently, it remains unclear which hypotheses have been a-priori specified and which have been brought up only after some data analysis. This, of course, is scientific malpractice because it does not enable the readership to assess the random error emerging from explorative data analysis. Accordingly, the variance of results across statistical methods is often misused to filter out the analysis that yields a significant result (“ p -hacking”, [ 28 ]). Pre-planned tests (via writing a grant) leave at least less room for p-hacking because they specify a-priori which analysis is to be conducted.

On the other hand, post-hoc analyses can be extremely useful for identifying unexpected phenomena and creating new hypotheses. Verbalization in the discussion section should therefore sharply separate between conclusions from hypothesis testing and new hypotheses created from data exploration. The distinction is profound, since a newly proposed hypothesis just makes a new claim. Suggesting new hypotheses cannot be wrong, this can only be inefficient if many hypotheses turn out to be wrong. Therefore, we suggest proposing only a limited number of new hypotheses that appear promising to stimulate further research and scientific progress. They are to be confirmed or falsified with future studies. A present discussion, however, should yet explicate the testable predictions a new hypothesis entails, and how a future study should be designed to keep bias in related analyses as small as possible.

Confidence intervals address the problem of reducing results to the dichotomy of significant and non-significant through providing a range of values that are compatible with the data at the given confidence level, usually 95% [ 29 ].

This is also addressed by Bayesian statistics that allows calculating what frequentist p -values are often misinterpreted to be: the probability that the alternative (or null) hypothesis is true [ 17 ]. Moreover, one can calculate how likely it is that the parameter lies within any specified range (e.g. the risk difference being greater than .05, a lower boundary for practical significance) [ 15 , 16 ]. To gain these benefits, one needs to specify how the parameter of interest (e.g. causal risk difference between D and LC) is distributed before inspecting the data. In Bayesian statistics (unlike frequentist statistics) a parameter is a random number that expresses prior beliefs via a “prior distribution”. Such a “prior” is combined with the data result to a “posterior distribution”. This integrates both sources of information.

Note that confidence intervals also can be interpreted from the Bayesian perspective (then called “credibility interval”). This assumes that all parameter values were equally likely (uniformly distributed, strictly speaking) before analyzing the data [ 5 , 6 , 20 ].

Testing just for a non-zero association can only yield evidence for an association deviating from zero. A better indicator for the true impact of an effect/association for clinical, economic, political, or research purposes is its magnitude. If an association between D and LC after adjusting for age and gender has been discovered, then the knowledge of D has additional value in predicting an elevated LC probability beyond age and gender. However, there may be many other factors that stronger predict LC and thus should receive higher priority in a doctor’s assessment. Besides, if an association is small, it may yet be explained by modest (upward) bias. Especially large samples often yield significant results with little practical value. The p -value does not measure strength of association [ 17 ]. For instance, in a large sample, a Pearson correlation between two dimensional variables could equal 0.1 only but with a p -value <.001. A further problem arises if the significance threshold of .05 is weakened post-hoc to allow for “statistical trends” ( p between .05 and .10) because a result has “failed to reach significance” (this wording claims that there is truly an association. If this was known, no research would be necessary).

It is usually the statistician’s job to insist not only on removing the attention from pure statistical significance to confidence intervals or even Bayesian interpretation, but also to point out the necessity of a meaningful cutoff for practical significance. The substantive researcher then has to provide this cutoff.

Researchers should not draw conclusions that have not been explicitly tested for. For example, one may have found a positive association between D and LC (e.g. p  = .049), but this association is not significant (e.g. p  = .051), when adjusting for “health behavior”. This does not imply that “health behavior” “explains” the association (yet fully). The difference in magnitude of association in both analyses compared here (without and with adjustment on HB) may be very small and the difference in p -values (“borderline significance” after adjustment) likely to emerge from random error. This often applies to larger differences in p as well.

Investigators, however, might find patterns in their results that they consider worth mentioning for creating hypotheses. In the example above, adding the words “in the sample”, would clarify that they refer just to the difference of two point estimates . By default, “association” in hypotheses testing should mean “statistically significant association” (explorative analyses should instead refer to “suggestive associations”).

Conclusions

Some issues of discussing results not mentioned yet appear to require only substantive reasoning. For instance, Bradford Hill’s consideration on “plausibility” claims that a causal effect is more likely, if it is in line with biological (substantive) knowledge, or if a dose-response relation has been found [ 30 ]. However, the application of these considerations itself depends on the trueness of assumptions. For instance, bias might act differently across the dose of exposure (e.g. larger measurement error in outcome among those with higher dosage). As a consequence, a pattern observed across dose may mask a true or pretend a wrong dose-response relation [ 30 ]. This again has to be brought up by statistical expertise.

There are, however, some practical issues that hinder the cooperation we suggest. First, substantive researchers often feel discomfort when urged to make assumptions on the mechanisms behind the data, presumably because they fear to be wrong. Here, the statistician needs to insist: “If you are unable to make any assumptions, you cannot conclude anything!” And: “As a scientist you have to understand the processes that create your data.” See [ 31 ] for practical advice on how to arrive at meaningful assumptions.

Second, statisticians have long been skeptical against causal inference. Still, most of them focus solely on describing observed data with distributional models, probably because estimating causal effects has long been regarded as unfeasible with scientific methods. Training in causality remains rather new, since strict mathematical methods have been developed only in the last decades [ 7 ].

The cooperation could be improved if education in both fields focused on the insight that one cannot succeed without the other. Academic education should demonstrate that in-depth conclusions from data unavoidably involve prior beliefs. Such education should say: Data do not “speak for themselves”, because they “speak” only ambiguously and little, since they have been filtered through various biases [ 32 ]. The subjectivity introduced by addressing bias, however, unsettles many researchers. On the other hand, conventional frequentist statistics just pretends to be objective. Instead of accepting the variety of possible assumptions, it makes the absurd assumption of “no bias with probability of one”. Or it avoids causal conclusions at all if no randomized study is possible. This limits science to investigating just associations for all factors that can never be randomized (e.g. onset of depression). However, the alternative of Bayesian statistics and thinking are themselves prone to fundamental cognitive biases which should as well be subject of interdisciplinary teaching [ 33 ].

Readers may take this article as an invitation to read further papers’ discussions differently while evaluating our claims. Rather than sharing a provided conclusion (or not) they could ask themselves whether a discussion enables them to clearly specify why they share it (or not). If the result is uncertainty, this might motivate them to write their next discussion differently. The proposals made in this article could help shifting scientific debates to where they belong. Rather than arguing on misunderstandings caused by ambiguity in a conclusion’s assumptions one should argue on the assumptions themselves.

Acknowledgements

We acknowledge support by the German Research Foundation and the Open Access Publication Funds of the TU Dresden. We wish to thank Pia Grabbe and Helen Steiner for language editing and the cited authors for their outstanding work that our proposals build on.

John Venz is funded by the German Federal Ministry of Education and Research (BMBF) project no. 01ER1303 and 01ER1703. He has contributed to this manuscript outside of time funded by these projects.

Availability of data and materials

Abbreviations, authors’ contributions.

MH and RM had the initial idea on the article. MH has taken the lead in writing. JV has contributed to the statistical parts, especially the Bayesian aspects. RM has refined the paragraphs on statistical inference. ST joined later and has added many clarifications related to the perspective of the substantive researcher. All authors have contributed to the final wording of all sections and the article’s revision. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Consent for publication, competing interests.

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Organizing Academic Research Papers: 8. The Discussion

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

The purpose of the discussion is to interpret and describe the significance of your findings in light of what was already known about the research problem being investigated, and to explain any new understanding or fresh insights about the problem after you've taken the findings into consideration. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but it does not simply repeat or rearrange the introduction; the discussion should always explain how your study has moved the reader's understanding of the research problem forward from where you left them at the end of the introduction.

Importance of a Good Discussion

This section is often considered the most important part of a research paper because it most effectively demonstrates your ability as a researcher to think critically about an issue, to develop creative solutions to problems based on the findings, and to formulate a deeper, more profound understanding of the research problem you are studying.

The discussion section is where you explore the underlying meaning of your research , its possible implications in other areas of study, and the possible improvements that can be made in order to further develop the concerns of your research.

This is the section where you need to present the importance of your study and how it may be able to contribute to and/or fill existing gaps in the field. If appropriate, the discussion section is also where you state how the findings from your study revealed new gaps in the literature that had not been previously exposed or adequately described.

This part of the paper is not strictly governed by objective reporting of information but, rather, it is where you can engage in creative thinking about issues through evidence-based interpretation of findings. This is where you infuse your results with meaning.

Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section . San Francisco Edit, 2003-2008.

Structure and Writing Style

I.  General Rules

These are the general rules you should adopt when composing your discussion of the results :

• Do not be verbose or repetitive.
• Be concise and make your points clearly.
• Avoid using jargon.
• Follow a logical stream of thought.
• Use the present verb tense, especially for established facts; however, refer to specific works and references in the past tense.
• If needed, use subheadings to help organize your presentation or to group your interpretations into themes.

II.  The Content

The content of the discussion section of your paper most often includes :

• Explanation of results : comment on whether or not the results were expected and present explanations for the results; go into greater depth when explaining findings that were unexpected or especially profound. If appropriate, note any unusual or unanticipated patterns or trends that emerged from your results and explain their meaning.
• References to previous research : compare your results with the findings from other studies, or use the studies to support a claim. This can include re-visiting key sources already cited in your literature review section, or, save them to cite later in the discussion section if they are more important to compare with your results than being part of the general research you cited to provide context and background information.
• Deduction : a claim for how the results can be applied more generally. For example, describing lessons learned, proposing recommendations that can help improve a situation, or recommending best practices.
• Hypothesis : a more general claim or possible conclusion arising from the results [which may be proved or disproved in subsequent research].

III. Organization and Structure

Keep the following sequential points in mind as you organize and write the discussion section of your paper:

• Think of your discussion as an inverted pyramid. Organize the discussion from the general to the specific, linking your findings to the literature, then to theory, then to practice [if appropriate].
• Use the same key terms, mode of narration, and verb tense [present] that you used when when describing the research problem in the introduction.
• Begin by briefly re-stating the research problem you were investigating and answer all of the research questions underpinning the problem that you posed in the introduction.
• Describe the patterns, principles, and relationships shown by each major findings and place them in proper perspective. The sequencing of providing this information is important; first state the answer, then the relevant results, then cite the work of others. If appropriate, refer the reader to a figure or table to help enhance the interpretation of the data. The order of interpreting each major finding should be in the same order as they were described in your results section.
• A good discussion section includes analysis of any unexpected findings. This paragraph should begin with a description of the unexpected finding, followed by a brief interpretation as to why you believe it appeared and, if necessary, its possible significance in relation to the overall study. If more than one unexpected finding emerged during the study, describe each them in the order they appeared as you gathered the data.
• Before concluding the discussion, identify potential limitations and weaknesses. Comment on their relative importance in relation to your overall interpretation of the results and, if necessary, note how they may affect the validity of the findings. Avoid using an apologetic tone; however, be honest and self-critical.
• The discussion section should end with a concise summary of the principal implications of the findings regardless of statistical significance. Give a brief explanation about why you believe the findings and conclusions of your study are important and how they support broader knowledge or understanding of the research problem. This can be followed by any recommendations for further research. However, do not offer recommendations which could have been easily addressed within the study. This demonstrates to the reader you have inadequately examined and interpreted the data.

IV.  Overall Objectives

The objectives of your discussion section should include the following: I.  Reiterate the Research Problem/State the Major Findings

Briefly reiterate for your readers the research problem or problems you are investigating and the methods you used to investigate them, then move quickly to describe the major findings of the study. You should write a direct, declarative, and succinct proclamation of the study results.

II.  Explain the Meaning of the Findings and Why They are Important

No one has thought as long and hard about your study as you have. Systematically explain the meaning of the findings and why you believe they are important. After reading the discussion section, you want the reader to think about the results [“why hadn’t I thought of that?”]. You don’t want to force the reader to go through the paper multiple times to figure out what it all means. Begin this part of the section by repeating what you consider to be your most important finding first.

III.  Relate the Findings to Similar Studies

No study is so novel or possesses such a restricted focus that it has absolutely no relation to other previously published research. The discussion section should relate your study findings to those of other studies, particularly if questions raised by previous studies served as the motivation for your study, the findings of other studies support your findings [which strengthens the importance of your study results], and/or they point out how your study differs from other similar studies. IV.  Consider Alternative Explanations of the Findings

It is important to remember that the purpose of research is to discover and not to prove . When writing the discussion section, you should carefully consider all possible explanations for the study results, rather than just those that fit your prior assumptions or biases.

V.  Acknowledge the Study’s Limitations

It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor! Describe the generalizability of your results to other situations, if applicable to the method chosen, then describe in detail problems you encountered in the method(s) you used to gather information. Note any unanswered questions or issues your study did not address, and.... VI.  Make Suggestions for Further Research

Although your study may offer important insights about the research problem, other questions related to the problem likely remain unanswered. Moreover, some unanswered questions may have become more focused because of your study. You should make suggestions for further research in the discussion section.

NOTE: Besides the literature review section, the preponderance of references to sources in your research paper are usually found in the discussion section . A few historical references may be helpful for perspective but most of the references should be relatively recent and included to aid in the interpretation of your results and/or linked to similar studies. If a study that you cited disagrees with your findings, don't ignore it--clearly explain why the study's findings differ from yours.

V.  Problems to Avoid

• Do not waste entire sentences restating your results . Should you need to remind the reader of the finding to be discussed, use "bridge sentences" that relate the result to the interpretation. An example would be: “The lack of available housing to single women with children in rural areas of Texas suggests that...[then move to the interpretation of this finding].”
• Recommendations for further research can be included in either the discussion or conclusion of your paper but do not repeat your recommendations in the both sections.
• Do not introduce new results in the discussion. Be wary of mistaking the reiteration of a specific finding for an interpretation.
• Use of the first person is acceptable, but too much use of the first person may actually distract the reader from the main points.

Analyzing vs. Summarizing. Department of English Writing Guide. George Mason University; Discussion . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Hess, Dean R. How to Write an Effective Discussion. Respiratory Care 49 (October 2004); Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section . San Francisco Edit, 2003-2008; The Lab Report . University College Writing Centre. University of Toronto; Summary: Using it Wisely . The Writing Center. University of North Carolina; Schafer, Mickey S. Writing the Discussion . Writing in Psychology course syllabus. University of Florida; Yellin, Linda L. A Sociology Writer's Guide. Boston, MA: Allyn and Bacon, 2009.

Writing Tip

Don’t Overinterpret the Results!

Interpretation is a subjective exercise. Therefore, be careful that you do not read more into the findings than can be supported by the evidence you've gathered. Remember that the data are the data: nothing more, nothing less.

Another Writing Tip

Don't Write Two Results Sections!

One of the most common mistakes that you can make when discussing the results of your study is to present a superficial interpretation of the findings that more or less re-states the results section of your paper. Obviously, you must refer to your results when discussing them, but focus on the interpretion of those results, not just the data itself.

Azar, Beth. Discussing Your Findings.  American Psychological Association gradPSYCH Magazine (January 2006)

Yet Another Writing Tip

Avoid Unwarranted Speculation!

The discussion section should remain focused on the findings of your study. For example, if you studied the impact of foreign aid on increasing levels of education among the poor in Bangladesh, it's generally not appropriate to speculate about how your findings might apply to populations in other countries without drawing from existing studies to support your claim. If you feel compelled to speculate, be certain that you clearly identify your comments as speculation or as a suggestion for where further research is needed. Sometimes your professor will encourage you to expand the discussion in this way, while others don’t care what your opinion is beyond your efforts to interpret the data.

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How To Write The Discussion Chapter

A Simple Explainer With Examples + Free Template

By: Jenna Crossley (PhD) | Reviewed By: Dr. Eunice Rautenbach | August 2021

If you’re reading this, chances are you’ve reached the discussion chapter of your thesis or dissertation and are looking for a bit of guidance. Well, you’ve come to the right place ! In this post, we’ll unpack and demystify the typical discussion chapter in straightforward, easy to understand language, with loads of examples .

Overview: The Discussion Chapter

• What  the discussion chapter is
• What to include in your discussion
• How to write up your discussion
• A few tips and tricks to help you along the way
• Free discussion template

What (exactly) is the discussion chapter?

The discussion chapter is where you interpret and explain your results within your thesis or dissertation. This contrasts with the results chapter, where you merely present and describe the analysis findings (whether qualitative or quantitative ). In the discussion chapter, you elaborate on and evaluate your research findings, and discuss the significance and implications of your results .

In this chapter, you’ll situate your research findings in terms of your research questions or hypotheses and tie them back to previous studies and literature (which you would have covered in your literature review chapter). You’ll also have a look at how relevant and/or significant your findings are to your field of research, and you’ll argue for the conclusions that you draw from your analysis. Simply put, the discussion chapter is there for you to interact with and explain your research findings in a thorough and coherent manner.

What should I include in the discussion chapter?

First things first: in some studies, the results and discussion chapter are combined into one chapter .  This depends on the type of study you conducted (i.e., the nature of the study and methodology adopted), as well as the standards set by the university.  So, check in with your university regarding their norms and expectations before getting started. In this post, we’ll treat the two chapters as separate, as this is most common.

Basically, your discussion chapter should analyse , explore the meaning and identify the importance of the data you presented in your results chapter. In the discussion chapter, you’ll give your results some form of meaning by evaluating and interpreting them. This will help answer your research questions, achieve your research aims and support your overall conclusion (s). Therefore, you discussion chapter should focus on findings that are directly connected to your research aims and questions. Don’t waste precious time and word count on findings that are not central to the purpose of your research project.

As this chapter is a reflection of your results chapter, it’s vital that you don’t report any new findings . In other words, you can’t present claims here if you didn’t present the relevant data in the results chapter first.  So, make sure that for every discussion point you raise in this chapter, you’ve covered the respective data analysis in the results chapter. If you haven’t, you’ll need to go back and adjust your results chapter accordingly.

If you’re struggling to get started, try writing down a bullet point list everything you found in your results chapter. From this, you can make a list of everything you need to cover in your discussion chapter. Also, make sure you revisit your research questions or hypotheses and incorporate the relevant discussion to address these.  This will also help you to see how you can structure your chapter logically.

Need a helping hand?

How to write the discussion chapter

Now that you’ve got a clear idea of what the discussion chapter is and what it needs to include, let’s look at how you can go about structuring this critically important chapter. Broadly speaking, there are six core components that need to be included, and these can be treated as steps in the chapter writing process.

Step 1: Restate your research problem and research questions

The first step in writing up your discussion chapter is to remind your reader of your research problem , as well as your research aim(s) and research questions . If you have hypotheses, you can also briefly mention these. This “reminder” is very important because, after reading dozens of pages, the reader may have forgotten the original point of your research or been swayed in another direction. It’s also likely that some readers skip straight to your discussion chapter from the introduction chapter , so make sure that your research aims and research questions are clear.

Step 2: Summarise your key findings

Next, you’ll want to summarise your key findings from your results chapter. This may look different for qualitative and quantitative research , where qualitative research may report on themes and relationships, whereas quantitative research may touch on correlations and causal relationships. Regardless of the methodology, in this section you need to highlight the overall key findings in relation to your research questions.

Typically, this section only requires one or two paragraphs , depending on how many research questions you have. Aim to be concise here, as you will unpack these findings in more detail later in the chapter. For now, a few lines that directly address your research questions are all that you need.

Some examples of the kind of language you’d use here include:

• The data suggest that…
• The data support/oppose the theory that…
• The analysis identifies…

These are purely examples. What you present here will be completely dependent on your original research questions, so make sure that you are led by them .

Step 3: Interpret your results

Once you’ve restated your research problem and research question(s) and briefly presented your key findings, you can unpack your findings by interpreting your results. Remember: only include what you reported in your results section – don’t introduce new information.

From a structural perspective, it can be a wise approach to follow a similar structure in this chapter as you did in your results chapter. This would help improve readability and make it easier for your reader to follow your arguments. For example, if you structured you results discussion by qualitative themes, it may make sense to do the same here.

Alternatively, you may structure this chapter by research questions, or based on an overarching theoretical framework that your study revolved around. Every study is different, so you’ll need to assess what structure works best for you.

When interpreting your results, you’ll want to assess how your findings compare to those of the existing research (from your literature review chapter). Even if your findings contrast with the existing research, you need to include these in your discussion. In fact, those contrasts are often the most interesting findings . In this case, you’d want to think about why you didn’t find what you were expecting in your data and what the significance of this contrast is.

Here are a few questions to help guide your discussion:

• How do your results relate with those of previous studies ?
• If you get results that differ from those of previous studies, why may this be the case?
• What do your results contribute to your field of research?
• What other explanations could there be for your findings?

When interpreting your findings, be careful not to draw conclusions that aren’t substantiated . Every claim you make needs to be backed up with evidence or findings from the data (and that data needs to be presented in the previous chapter – results). This can look different for different studies; qualitative data may require quotes as evidence, whereas quantitative data would use statistical methods and tests. Whatever the case, every claim you make needs to be strongly backed up.

Step 4: Acknowledge the limitations of your study

The fourth step in writing up your discussion chapter is to acknowledge the limitations of the study. These limitations can cover any part of your study , from the scope or theoretical basis to the analysis method(s) or sample. For example, you may find that you collected data from a very small sample with unique characteristics, which would mean that you are unable to generalise your results to the broader population.

For some students, discussing the limitations of their work can feel a little bit self-defeating . This is a misconception, as a core indicator of high-quality research is its ability to accurately identify its weaknesses. In other words, accurately stating the limitations of your work is a strength, not a weakness . All that said, be careful not to undermine your own research. Tell the reader what limitations exist and what improvements could be made, but also remind them of the value of your study despite its limitations.

Step 5: Make recommendations for implementation and future research

Now that you’ve unpacked your findings and acknowledge the limitations thereof, the next thing you’ll need to do is reflect on your study in terms of two factors:

• The practical application of your findings
• Suggestions for future research

The first thing to discuss is how your findings can be used in the real world – in other words, what contribution can they make to the field or industry? Where are these contributions applicable, how and why? For example, if your research is on communication in health settings, in what ways can your findings be applied to the context of a hospital or medical clinic? Make sure that you spell this out for your reader in practical terms, but also be realistic and make sure that any applications are feasible.

The next discussion point is the opportunity for future research . In other words, how can other studies build on what you’ve found and also improve the findings by overcoming some of the limitations in your study (which you discussed a little earlier). In doing this, you’ll want to investigate whether your results fit in with findings of previous research, and if not, why this may be the case. For example, are there any factors that you didn’t consider in your study? What future research can be done to remedy this? When you write up your suggestions, make sure that you don’t just say that more research is needed on the topic, also comment on how the research can build on your study.

Step 6: Provide a concluding summary

Finally, you’ve reached your final stretch. In this section, you’ll want to provide a brief recap of the key findings – in other words, the findings that directly address your research questions . Basically, your conclusion should tell the reader what your study has found, and what they need to take away from reading your report.

When writing up your concluding summary, bear in mind that some readers may skip straight to this section from the beginning of the chapter.  So, make sure that this section flows well from and has a strong connection to the opening section of the chapter.

Tips and tricks for an A-grade discussion chapter

Now that you know what the discussion chapter is , what to include and exclude , and how to structure it , here are some tips and suggestions to help you craft a quality discussion chapter.

• When you write up your discussion chapter, make sure that you keep it consistent with your introduction chapter , as some readers will skip from the introduction chapter directly to the discussion chapter. Your discussion should use the same tense as your introduction, and it should also make use of the same key terms.
• Don’t make assumptions about your readers. As a writer, you have hands-on experience with the data and so it can be easy to present it in an over-simplified manner. Make sure that you spell out your findings and interpretations for the intelligent layman.
• Have a look at other theses and dissertations from your institution, especially the discussion sections. This will help you to understand the standards and conventions of your university, and you’ll also get a good idea of how others have structured their discussion chapters. You can also check out our chapter template .
• Avoid using absolute terms such as “These results prove that…”, rather make use of terms such as “suggest” or “indicate”, where you could say, “These results suggest that…” or “These results indicate…”. It is highly unlikely that a dissertation or thesis will scientifically prove something (due to a variety of resource constraints), so be humble in your language.
• Use well-structured and consistently formatted headings to ensure that your reader can easily navigate between sections, and so that your chapter flows logically and coherently.

If you have any questions or thoughts regarding this post, feel free to leave a comment below. Also, if you’re looking for one-on-one help with your discussion chapter (or thesis in general), consider booking a free consultation with one of our highly experienced Grad Coaches to discuss how we can help you.

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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36 Comments

Thank you this is helpful!

This is very helpful to me… Thanks a lot for sharing this with us 😊

This has been very helpful indeed. Thank you.

This is actually really helpful, I just stumbled upon it. Very happy that I found it, thank you.

Me too! I was kinda lost on how to approach my discussion chapter. How helpful! Thanks a lot!

This is really good and explicit. Thanks

Thank you, this blog has been such a help.

Thank you. This is very helpful.

Dear sir/madame

Thanks a lot for this helpful blog. Really, it supported me in writing my discussion chapter while I was totally unaware about its structure and method of writing.

With regards

Syed Firoz Ahmad PhD, Research Scholar

I agree so much. This blog was god sent. It assisted me so much while I was totally clueless about the context and the know-how. Now I am fully aware of what I am to do and how I am to do it.

Thanks! This is helpful!

thanks alot for this informative website

Dear Sir/Madam,

Truly, your article was much benefited when i structured my discussion chapter.

Thank you very much!!!

This is helpful for me in writing my research discussion component. I have to copy this text on Microsoft word cause of my weakness that I cannot be able to read the text on screen a long time. So many thanks for this articles.

This was helpful

Thanks Jenna, well explained.

Thank you! This is super helpful.

Thanks very much. I have appreciated the six steps on writing the Discussion chapter which are (i) Restating the research problem and questions (ii) Summarising the key findings (iii) Interpreting the results linked to relating to previous results in positive and negative ways; explaining whay different or same and contribution to field of research and expalnation of findings (iv) Acknowledgeing limitations (v) Recommendations for implementation and future resaerch and finally (vi) Providing a conscluding summary

My two questions are: 1. On step 1 and 2 can it be the overall or you restate and sumamrise on each findings based on the reaerch question? 2. On 4 and 5 do you do the acknowlledgement , recommendations on each research finding or overall. This is not clear from your expalanattion.

Please respond.

This post is very useful. I’m wondering whether practical implications must be introduced in the Discussion section or in the Conclusion section?

Sigh, I never knew a 20 min video could have literally save my life like this. I found this at the right time!!!! Everything I need to know in one video thanks a mil ! OMGG and that 6 step!!!!!! was the cherry on top the cake!!!!!!!!!

Thanks alot.., I have gained much

This piece is very helpful on how to go about my discussion section. I can always recommend GradCoach research guides for colleagues.

Many thanks for this resource. It has been very helpful to me. I was finding it hard to even write the first sentence. Much appreciated.

Thanks so much. Very helpful to know what is included in the discussion section

this was a very helpful and useful information

This is very helpful. Very very helpful. Thanks for sharing this online!

it is very helpfull article, and i will recommend it to my fellow students. Thank you.

Superlative! More grease to your elbows.

Powerful, thank you for sharing.

Wow! Just wow! God bless the day I stumbled upon you guys’ YouTube videos! It’s been truly life changing and anxiety about my report that is due in less than a month has subsided significantly!

Simplified explanation. Well done.

The presentation is enlightening. Thank you very much.

Thanks for the support and guidance

This has been a great help to me and thank you do much

I second that “it is highly unlikely that a dissertation or thesis will scientifically prove something”; although, could you enlighten us on that comment and elaborate more please?

Sure, no problem.

Scientific proof is generally considered a very strong assertion that something is definitively and universally true. In most scientific disciplines, especially within the realms of natural and social sciences, absolute proof is very rare. Instead, researchers aim to provide evidence that supports or rejects hypotheses. This evidence increases or decreases the likelihood that a particular theory is correct, but it rarely proves something in the absolute sense.

Dissertations and theses, as substantial as they are, typically focus on exploring a specific question or problem within a larger field of study. They contribute to a broader conversation and body of knowledge. The aim is often to provide detailed insight, extend understanding, and suggest directions for further research rather than to offer definitive proof. These academic works are part of a cumulative process of knowledge building where each piece of research connects with others to gradually enhance our understanding of complex phenomena.

Furthermore, the rigorous nature of scientific inquiry involves continuous testing, validation, and potential refutation of ideas. What might be considered a “proof” at one point can later be challenged by new evidence or alternative interpretations. Therefore, the language of “proof” is cautiously used in academic circles to maintain scientific integrity and humility.

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Writing a scientific paper.

• Writing a lab report
• INTRODUCTION

Writing a "good" discussion section

"discussion and conclusions checklist" from: how to write a good scientific paper. chris a. mack. spie. 2018., peer review.

• LITERATURE CITED
• Bibliography of guides to scientific writing and presenting
• Presentations
• Lab Report Writing Guides on the Web

This is is usually the hardest section to write. You are trying to bring out the true meaning of your data without being too long. Do not use words to conceal your facts or reasoning. Also do not repeat your results, this is a discussion.

• Present principles, relationships and generalizations shown by the results
• Point out exceptions or lack of correlations. Define why you think this is so.
• Show how your results agree or disagree with previously published works
• Discuss the theoretical implications of your work as well as practical applications
• State your conclusions clearly. Summarize your evidence for each conclusion.
• Discuss the significance of the results
•  Evidence does not explain itself; the results must be presented and then explained.
• Typical stages in the discussion: summarizing the results, discussing whether results are expected or unexpected, comparing these results to previous work, interpreting and explaining the results (often by comparison to a theory or model), and hypothesizing about their generality.
• Discuss any problems or shortcomings encountered during the course of the work.
• Discuss possible alternate explanations for the results.
• Avoid: presenting results that are never discussed; presenting discussion that does not relate to any of the results; presenting results and discussion in chronological order rather than logical order; ignoring results that do not support the conclusions; drawing conclusions from results without logical arguments to back them up. 

CONCLUSIONS

• Provide a very brief summary of the Results and Discussion.
• Emphasize the implications of the findings, explaining how the work is significant and providing the key message(s) the author wishes to convey.
• Provide the most general claims that can be supported by the evidence.
• Provide a future perspective on the work.
• Avoid: repeating the abstract; repeating background information from the Introduction; introducing new evidence or new arguments not found in the Results and Discussion; repeating the arguments made in the Results and Discussion; failing to address all of the research questions set out in the Introduction. 

WHAT HAPPENS AFTER I COMPLETE MY PAPER?

 The peer review process is the quality control step in the publication of ideas.  Papers that are submitted to a journal for publication are sent out to several scientists (peers) who look carefully at the paper to see if it is "good science".  These reviewers then recommend to the editor of a journal whether or not a paper should be published. Most journals have publication guidelines. Ask for them and follow them exactly.    Peer reviewers examine the soundness of the materials and methods section.  Are the materials and methods used written clearly enough for another scientist to reproduce the experiment?  Other areas they look at are: originality of research, significance of research question studied, soundness of the discussion and interpretation, correct spelling and use of technical terms, and length of the article.

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General Research Paper Guidelines: Discussion

Discussion section.

The overall purpose of a research paper’s discussion section is to evaluate and interpret results, while explaining both the implications and limitations of your findings. Per APA (2020) guidelines, this section requires you to “examine, interpret, and qualify the results and draw inferences and conclusions from them” (p. 89). Discussion sections also require you to detail any new insights, think through areas for future research, highlight the work that still needs to be done to further your topic, and provide a clear conclusion to your research paper. In a good discussion section, you should do the following:

• Clearly connect the discussion of your results to your introduction, including your central argument, thesis, or problem statement.
• Provide readers with a critical thinking through of your results, answering the “so what?” question about each of your findings. In other words, why is this finding important?
• Detail how your research findings might address critical gaps or problems in your field
• Compare your results to similar studies’ findings
• Provide the possibility of alternative interpretations, as your goal as a researcher is to “discover” and “examine” and not to “prove” or “disprove.” Instead of trying to fit your results into your hypothesis, critically engage with alternative interpretations to your results.

For more specific details on your Discussion section, be sure to review Sections 3.8 (pp. 89-90) and 3.16 (pp. 103-104) of your 7 th edition APA manual

*Box content adapted from:

University of Southern California (n.d.). Organizing your social sciences research paper: 8 the discussion . https://libguides.usc.edu/writingguide/discussion

Limitations

Limitations of generalizability or utility of findings, often over which the researcher has no control, should be detailed in your Discussion section. Including limitations for your reader allows you to demonstrate you have thought critically about your given topic, understood relevant literature addressing your topic, and chosen the methodology most appropriate for your research. It also allows you an opportunity to suggest avenues for future research on your topic. An effective limitations section will include the following:

• Detail (a) sources of potential bias, (b) possible imprecision of measures, (c) other limitations or weaknesses of the study, including any methodological or researcher limitations.
• Sample size: In quantitative research, if a sample size is too small, it is more difficult to generalize results.
• Lack of available/reliable data : In some cases, data might not be available or reliable, which will ultimately affect the overall scope of your research. Use this as an opportunity to explain areas for future study.
• Lack of prior research on your study topic: In some cases, you might find that there is very little or no similar research on your study topic, which hinders the credibility and scope of your own research. If this is the case, use this limitation as an opportunity to call for future research. However, make sure you have done a thorough search of the available literature before making this claim.
• Flaws in measurement of data: Hindsight is 20/20, and you might realize after you have completed your research that the data tool you used actually limited the scope or results of your study in some way. Again, acknowledge the weakness and use it as an opportunity to highlight areas for future study.
• Limits of self-reported data: In your research, you are assuming that any participants will be honest and forthcoming with responses or information they provide to you. Simply acknowledging this assumption as a possible limitation is important in your research.
• Access: Most research requires that you have access to people, documents, organizations, etc.. However, for various reasons, access is sometimes limited or denied altogether. If this is the case, you will want to acknowledge access as a limitation to your research.
• Time: Choosing a research focus that is narrow enough in scope to finish in a given time period is important. If such limitations of time prevent you from certain forms of research, access, or study designs, acknowledging this time restraint is important. Acknowledging such limitations is important, as they can point other researchers to areas that require future study.
• Potential Bias: All researchers have some biases, so when reading and revising your draft, pay special attention to the possibilities for bias in your own work. Such bias could be in the form you organized people, places, participants, or events. They might also exist in the method you selected or the interpretation of your results. Acknowledging such bias is an important part of the research process.
• Language Fluency: On occasion, researchers or research participants might have language fluency issues, which could potentially hinder results or how effectively you interpret results. If this is an issue in your research, make sure to acknowledge it in your limitations section.

University of Southern California (n.d.). Organizing your social sciences research paper: Limitations of the study . https://libguides.usc.edu/writingguide/limitations

In many research papers, the conclusion, like the limitations section, is folded into the larger discussion section. If you are unsure whether to include the conclusion as part of your discussion or as a separate section, be sure to defer to the assignment instructions or ask your instructor.

The conclusion is important, as it is specifically designed to highlight your research’s larger importance outside of the specific results of your study. Your conclusion section allows you to reiterate the main findings of your study, highlight their importance, and point out areas for future research. Based on the scope of your paper, your conclusion could be anywhere from one to three paragraphs long. An effective conclusion section should include the following:

• Describe the possibilities for continued research on your topic, including what might be improved, adapted, or added to ensure useful and informed future research.
• Provide a detailed account of the importance of your findings
• Reiterate why your problem is important, detail how your interpretation of results impacts the subfield of study, and what larger issues both within and outside of your field might be affected from such results

University of Southern California (n.d.). Organizing your social sciences research paper: 9. the conclusion . https://libguides.usc.edu/writingguide/conclusion

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Research Paper Guide

Research Paper Discussion Section

How To Write A Discussion For A Research Paper | Examples & Tips

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How to Write a Research Methodology for a Research Paper

Ever find yourself stuck when trying to write the discussion part of your research paper? Don't worry, it happens to a lot of people. 

The discussion section is super important in your research paper . It's where you explain what your results mean. But turning all that data into a clear and meaningful story? That's not easy.

Guess what? MyPerfectWords.com has come up with a solution. 

This blog is your guide to writing an outstanding discussion section. We'll guide you step by step with useful tips to make sure your research stands out.

So, let’s get started!

• 1. What Exactly is a Discussion Section in the Research Paper?
• 2. How to Write the Discussion Section of a Research Paper?
• 3. Examples of Good Discussion for a Research Paper
• 4. Mistakes to Avoid in Your Research Paper's Discussion 

What Exactly is a Discussion Section in the Research Paper?

In a research paper, the discussion section is where you explain what your results really mean. It's like answering the questions, "So what?" and "What's the big picture?" 

The discussion section is your chance to help your readers understand why your findings are important and how they fit into the larger context. It's more than just summarizing; it's about making your research understandable and meaningful to others.

Importance of the Discussion Section

The discussion section isn't just a formality; it's the heart of your research paper. This is where your findings transform from data into knowledge. 

Let's break down why it's so crucial:

• Interpretation of Results : The discussion is where you get to tell readers what your results really mean. You go into the details, helping them understand the story behind the numbers or findings.
• Connecting the Dots : You connect different parts of your research, showing how they relate. This helps your readers see the bigger picture.
• Relevance to the Big Picture : You get to highlight why your research matters. How does it contribute to the broader understanding of the topic? This is your time to make your research significant.
• Addressing Limitations : In the discussion, you can acknowledge any limitations in your study and discuss how they might impact your results.
• Suggestions for Further Research : The discussion is where you suggest areas for future exploration. It's like passing the baton to the next researcher, indicating where more work could be done.

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How to Write the Discussion Section of a Research Paper?

The Discussion section in a research paper plays a vital role in interpreting findings and formulating a conclusion . Given below are the main components of the discussion section:

• Quick Summary: A brief recap of your main findings.
• Interpretation: Significance and meaning of your results in relation to your research question.
• Literature Review : Connecting your findings with previous research or similar studies.
• Limitations: Discussing any study limitations, addressing potential concerns.
• Implications: Broader implications of your findings, considering practical and theoretical aspects.
• Alternative Explanations: Evaluating alternative interpretations, demonstrating a comprehensive analysis.
• Connecting to Hypotheses : Summarizing how your result section aligns or diverges from your initial hypotheses.

Now let’s explore the steps to write an effective discussion section that will effectively communicate the significance of your research:

Step 1: Get Started with a Quick Summary

Start by quickly telling your readers the main things you found in your research. Don't explain them in detail just yet; just give a simple overview. 

This helps your readers get the big picture before diving into the details.

Step 2: Interpret Your Results

In the next step, talk about what your findings really mean. Share why the information you gathered is important. Connect each result to the questions you were trying to answer and the goals you set for your research.

Step 3: Relate to Existing Literature

In this step, link up your discoveries with what other researchers have already figured out. 

Share if your results are similar to or different from what's been found before. This helps give more background to your study and shows you know what other scientists have been up to.

Step 4: Address Limitations Honestly

Every study has its limitations. Acknowledge them openly in your discussion. This not only shows transparency but also helps readers interpret your results more accurately.

Step 5: Discuss the Implications

Explore the implications of your findings. How do they contribute to the field? What real-world applications or changes might they suggest?

Dig into why your discoveries are important. How do they help the subject you studied? 

This step is like looking at the bigger picture and asking, "So, what can we do with this information?"

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Step 6: Consider Alternative Explanations

After discussing the implications, challenge yourself by exploring alternative explanations for your results. 

Discuss different perspectives and show that you've considered multiple angles.

Step 7: Connect to Your Hypotheses or Research Questions

For the last step, revisit your initial hypotheses or research questions. Explain whether your results support what you thought might happen or if they surprised you. 

Examples of Good Discussion for a Research Paper

Learning from well-crafted discussions can significantly enhance your own writing. Given below are some examples to help you understand how to write your own.

Discussion for a Research Paper Example Pdf

Discussion for a Medical Research Paper

Discussion Section for a Qualitative Research Paper

Mistakes to Avoid in Your Research Paper's Discussion 

Writing the discussion section of your research paper can be tricky. To make sure you're on the right track, be mindful of these common mistakes:

• Overstating or Overinterpreting Results

Avoid making your findings sound more groundbreaking than they are. Stick to what your data actually shows, and don't exaggerate.

• Neglecting Alternative Explanations 

Failing to consider other possible explanations for your results can weaken your discussion. Always explore alternative perspectives to present a well-rounded view.

• Ignoring Limitations 

Don't sweep limitations under the rug. Acknowledge them openly and discuss how they might affect the validity or generalizability of your results.

• Being Overly Technical or Jargon-laden

Remember that your audience may not be experts in your specific field. Avoid using overly technical language or excessive jargon that could alienate your readers.

• Disregarding the 'So What' Factor

Always explain the significance of your findings. Don't leave your readers wondering why your research matters or how it contributes to the broader understanding of the subject.

• Rushing the Conclusion

The conclusion section of your discussion is critical. Don't rush it. Summarize the key points and leave your readers with a strong understanding of the significance of your research.

So, there you have it —writing a discussion and conclusion section isn't easy, but avoiding some common mistakes can make it much smoother. 

Remember to keep it real with your results, think about what else could explain things, and don't forget about any limits in your study.

But if you're feeling stuck, MyPerfectWords.com is here for you. 

Our team of experts knows their way around discussions. Whether you need some guidance or want someone to handle the writing for you, we've got your back.

Don't let discussion writing stress you out. Let our essay writing service for college  make your academic life easier.

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The discussion section of a research paper is where the author analyzes and explains the importance of the study's results. It presents the conclusions drawn from the study, compares them to previous research, and addresses any potential limitations or weaknesses. The discussion section should also suggest areas for future research.

Everything is not that complicated if you know where to find the required information. We’ll tell you everything there is to know about writing your discussion. Our easy guide covers all important bits, including research questions and your research results. Do you know how all enumerated events are connected? Well, you will after reading this guide we’ve prepared for you!

What Is in the Discussion Section of a Research Paper

The discussion section of a research paper can be viewed as something similar to the conclusion of your paper. But not literal, of course. It’s an ultimate section where you can talk about the findings of your study. Think about these questions when writing:

• Did you answer all of the promised research questions?
• Did you mention why your work matters?
• What are your findings, and why should anyone even care?
• Does your study have a literature review?

So, answer your questions, provide proof, and don’t forget about your promises from the introduction. 

How to Write a Discussion Section in 5 Steps

How to write the discussion section of a research paper is something everyone googles eventually. It's just life. But why not make everything easier? In brief, this section we’re talking about must include all following parts:

• Answers for research questions
• Literature review
• Results of the work
• Limitations of one’s study
• Overall conclusion

Indeed, all those parts may confuse anyone. So by looking at our guide, you'll save yourself some hassle.  P.S. All our steps are easy and explained in detail! But if you are looking for the most efficient solution, consider using professional help. Leave your “ write my research paper for me ” order at StudyCrumb and get a customized study tailored to your requirements.

Step 1. Start Strong: Discussion Section of a Research Paper

First and foremost, how to start the discussion section of a research paper? Here’s what you should definitely consider before settling down to start writing:

• All essays or papers must begin strong. All readers will not wait for any writer to get to the point. We advise summarizing the paper's main findings.
• Moreover, you should relate both discussion and literature review to what you have discovered. Mentioning that would be a plus too.
• Make sure that an introduction or start per se is clear and concise. Word count might be needed for school. But any paper should be understandable and not too diluted.

Step 2. Answer the Questions in Your Discussion Section of a Research Paper

Writing the discussion section of a research paper also involves mentioning your questions. Remember that in your introduction, you have promised your readers to answer certain questions. Well, now it’s a perfect time to finally give the awaited answer. You need to explain all possible correlations between your findings, research questions, and literature proposed. You already had hypotheses. So were they correct, or maybe you want to propose certain corrections? Section’s main goal is to avoid open ends. It’s not a story or a fairytale with an intriguing ending. If you have several questions, you must answer them. As simple as that.

Step 3. Relate Your Results in a Discussion Section

Writing a discussion section of a research paper also requires any writer to explain their results. You will undoubtedly include an impactful literature review. However, your readers should not just try and struggle with understanding what are some specific relationships behind previous studies and your results.  Your results should sound something like: “This guy in their paper discovered that apples are green. Nevertheless, I have proven via experimentation and research that apples are actually red.” Please, don’t take these results directly. It’s just an initial hypothesis. But what you should definitely remember is any practical implications of your study. Why does it matter and how can anyone use it? That’s the most crucial question.

Step 4. Describe the Limitations in Your Discussion Section

Discussion section of a research paper isn’t limitless. What does that mean? Essentially, it means that you also have to discuss any limitations of your study. Maybe you had some methodological inconsistencies. Possibly, there are no particular theories or not enough information for you to be entirely confident in one’s conclusions.  You might say that an available source of literature you have studied does not focus on one’s issue. That’s why one’s main limitation is theoretical. However, keep in mind that your limitations must possess a certain degree of relevancy. You can just say that you haven’t found enough books. Your information must be truthful to research.

Step 5. Conclude Your Discussion Section With Recommendations

Your last step when you write a discussion section in a paper is its conclusion, like in any other academic work. Writer’s conclusion must be as strong as their starting point of the overall work. Check out our brief list of things to know about the conclusion in research paper :

• It must present its scientific relevance and importance of your work.
• It should include different implications of your research.
• It should not, however, discuss anything new or things that you have not mentioned before.
• Leave no open questions and carefully complete the work without them.

Discussion Section of a Research Paper Example

All the best example discussion sections of a research paper will be written according to our brief guide. Don’t forget that you need to state your findings and underline the importance of your work. An undoubtedly big part of one’s discussion will definitely be answering and explaining the research questions. In other words, you’ll already have all the knowledge you have so carefully gathered. Our last step for you is to recollect and wrap up your paper. But we’re sure you’ll succeed!

How to Write a Discussion Section: Final Thoughts

Today we have covered how to write a discussion section. That was quite a brief journey, wasn’t it? Just to remind you to focus on these things:

• Importance of your study.
• Summary of the information you have gathered.
• Main findings and conclusions.
• Answers to all research questions without an open end.
• Correlation between literature review and your results.

But, wait, this guide is not the only thing we can do. Looking for how to write an abstract for a research paper  for example? We have such a blog and much more on our platform.

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Discussion Section of a Research Paper: Frequently Asked Questions

1. how long should the discussion section of a research paper be.

Our discussion section of a research paper should not be longer than other sections. So try to keep it short but as informative as possible. It usually contains around 6-7 paragraphs in length. It is enough to briefly summarize all the important data and not to drag it.

2. What's the difference between the discussion and the results?

The difference between discussion and results is very simple and easy to understand. The results only report your main findings. You stated what you have found and how you have done that. In contrast, one’s discussion mentions your findings and explains how they relate to other literature, research questions, and one’s hypothesis. Therefore, it is not only a report but an efficient as well as proper explanation.

3. What's the difference between a discussion and a conclusion?

The difference between discussion and conclusion is also quite easy. Conclusion is a brief summary of all the findings and results. Still, our favorite discussion section interprets and explains your main results. It is an important but more lengthy and wordy part. Besides, it uses extra literature for references.

4. What is the purpose of the discussion section?

The primary purpose of a discussion section is to interpret and describe all your interesting findings. Therefore, you should state what you have learned, whether your hypothesis was correct and how your results can be explained using other sources. If this section is clear to readers, our congratulations as you have succeeded.

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

Published on 21 August 2022 by Shona McCombes . Revised on 25 October 2022.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review , and making an argument in support of your overall conclusion . It should not be a second results section .

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary: A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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Table of contents

What not to include in your discussion section, step 1: summarise your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasise weaknesses or failures.

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Start this section by reiterating your research problem  and concisely summarising your major findings. Don’t just repeat all the data you have already reported – aim for a clear statement of the overall result that directly answers your main  research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that …
• The study demonstrates a correlation between …
• This analysis supports the theory that …
• The data suggest  that …

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualising your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organise your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis …
• Contrary to the hypothesised association …
• The results contradict the claims of Smith (2007) that …
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is x .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of …
• The results do not fit with the theory that …
• The experiment provides a new insight into the relationship between …
• These results should be taken into account when considering how to …
• The data contribute a clearer understanding of …
• While previous research has focused on  x , these results demonstrate that y .

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Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalisability is limited.
• If you encountered problems when gathering or analysing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalisability of the results is limited by …
• The reliability of these data is impacted by …
• Due to the lack of data on x , the results cannot confirm …
• The methodological choices were constrained by …
• It is beyond the scope of this study to …

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done – give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish …
• Future studies should take into account …
• Avenues for future research include …

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• Published: 11 May 2024

Exploration of the creative processes in animals, robots, and AI: who holds the authorship?

• Cédric Sueur 1 , 2 , 3 ,
• Jessica Lombard 4 ,
• Olivier Capra 3 ,
• Benjamin Beltzung 1 &
• Marie Pelé   ORCID: orcid.org/0000-0003-2297-5522 3  

Humanities and Social Sciences Communications volume  11 , Article number:  611 ( 2024 ) Cite this article

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Picture a simple scenario: a worm, in its modest way, traces a trail of paint as it moves across a sheet of paper. Now shift your imagination to a more complex scene, where a chimpanzee paints on another sheet of paper. A simple question arises: Do you perceive an identical creative process in these two animals? Can both of these animals be designated as authors of their creation? If only one, which one? This paper delves into the complexities of authorship, consciousness, and agency, unpacking the nuanced distinctions between such scenarios and exploring the underlying principles that define creative authorship across different forms of life. It becomes evident that attributing authorship to an animal hinges on its intention to create, an aspect intertwined with its agency and awareness of the creative act. These concepts are far from straightforward, as they traverse the complex landscapes of animal ethics and law. But our exploration does not stop there. Now imagine a robot, endowed with artificial intelligence, producing music. This prompts us to question how we should evaluate and perceive such creations. Is the creative process of a machine fundamentally different from that of an animal or a human? As we venture further into this realm of human-made intelligence, we confront an array of ethical, philosophical, and legal quandaries. This paper provides a platform for a reflective discussion: ethologists, neuroscientists, philosophers, and bioinformaticians converge in a multidisciplinary dialogue. Their insights provide valuable perspectives for establishing a foundation upon which to discuss the intricate concepts of authorship and appropriation concerning artistic works generated by non-human entities.

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In titling this article, we utilised the AI capabilities of ChatGPT, drawing upon our summary for guidance. This decision prompts a consideration of whether this AI ought to be acknowledged as one of the authors. Artistic authorship involves the recognition of an entity as the originator of a work that possesses aesthetic, cultural, or intellectual value. This concept is deeply rooted in philosophical debates about expression, identity, and the nature of art itself, while also engaging in legal discussions about copyright and ownership. The issues addressed in this context are poised to spark extensive debate in the future, bridging both artistic and scientific communities. This paper focuses on our research into the artistic output of non-human animals and machines, examining the extent to which their creations–ranging from pictures and paintings to music–are not acknowledged as their own work. Despite the evident capacity of these entities to produce what can be described as art, authorship is often not attributed to them. Instead, their creations are appropriated and monetised by humans, raising significant questions about the recognition of authorship and ownership in the context of non-human and artificial creators. For instance, in 2016, in Indonesia, a wild-crested macaque (later named Naruto) took a selfie with the camera of a professional photographer. This ‘selfie’ went viral worldwide and was quickly seen as a financial opportunity by the camera owner, who claimed the copyright. PETA (People for the Ethical Treatment of Animals) filed a lawsuit and initiated legal proceedings against the camera owner, seeking recognition of the sole monkey as the author of the photograph and demanding that copyright royalties be paid to Naruto if the image were to be used for commercial purposes. However, the legal vacuum surrounding non-human copyright and the fact that Naruto was not recognised as a legal person led the judge to reject PETA’s request (Guadamuz, 2016 ; Rosati, 2017 ).

This paper is the collaborative effort of a diverse team of researchers, including two ethologists, a neuroscientist, a philosopher, and a computer scientist. Our interdisciplinary approach is deliberate, reflecting our belief that the complex questions surrounding non-human authorship and rights in the context of animals, robots, and AI necessitate insights from multiple disciplines. Each author brings a unique perspective to the discussion, from the nuanced behaviours and cognitive abilities of animals to the ethical implications of emerging technologies and the philosophical underpinnings of creativity Footnote 1 and intelligence. Our collective expertise enables a comprehensive exploration of the subject matter, though it also means that our discussion flows through a wide range of arguments and disciplines. Recognising the importance of situatedness and positionality in scholarly work, we aim to locate our argumentation within the intersections of our respective fields, thereby providing a multifaceted view that enriches the debate on non-human entities’ potential for creativity and authorship. By clarifying our backgrounds and the intentional breadth of our perspectives, we hope to make explicit the relevance and intention behind our scholarly argumentation, ensuring our readers understand the foundation upon which our analysis is built.

Introduction

Just as toolmaking, sense of humour, or mathematics have been considered uniquely human activities, art has long been regarded as a distinctly human endeavour. However, without claiming to be Picasso or Mozart, some animals exhibit graphic or musical behaviours that we might classify as artistic (Watanabe, 2012 ). In Oceania, male bowerbirds build intricate structures from branches to attract females and secure opportunities for mating (Diamond, 1986 ). Pebbles, seeds, or leaves, often coloured, are placed by the birds at the entrance of the bower to master its symmetry, aesthetics, and perspective. Birds even adhere to a gradation of these different elements, from larger to smaller or from darker to lighter, to refine their decor. Also, in an effort to court females, male pufferfish sculpt rosettes in the sand with their mouths (Kawase et al., 2013 ). These sandy structures are likened to the lines and geoglyphs of Nazca, located in southern Peru and associated with the pre-Inca Nazca culture (Mujica, 2000 ). Another example of complex graphic compositions in animals that we could appreciate as art is undoubtedly the structure of spider webs (Krink and Vollrath, 1997 ). On the musical side, it would be difficult not to mention the complexity of the songs of the animals around us. In many bird species, males use their most beautiful songs to attract females for reproduction. Others have mastered the art of imitation, such as the lyrebird (Menura sp.) (Dalziell and Magrath, 2012 ). Among primates, the communication of gibbons (Hylobates sp.) is entirely based on their vocalisations which even allow them to recognise each other individually (Terleph et al., 2015 ; Oyakawa et al., 2007 ; Geissmann, 2000 ). The same is true for cetaceans like whales and dolphins (Janik, 2014 ). The creation of these complex graphic or vocal compositions involves both elaborate learning processes and simple rules that enhance the desired effectiveness of the produced structure. Like with computational algorithms, natural selection and sexual selection have shaped the processes behind these animal creations, which sometimes emotionally touch us and move us. The creative aspect of these examples lies in the animals’ ability to produce and modify sounds in ways that serve both functional and expressive purposes. The complexity of the songs, the individual recognition through vocalisation, and their structured, evolving nature, all point to a process that involves learning, innovation, and even cultural transmission among these animals. Such behaviours mirror the human capacity for creating, involving not just the application of simple rules but also the expression of complex emotions, social connections, and cultural identities. This complexity and depth of animal vocalisations underscore their value as creative productions, inviting us to broaden our understanding of creativity beyond human artistic endeavours. Eventually, it is not uncommon for some of these animal productions to become sources of artistic inspiration for humans. The French Olivier Messiaen, for example, was one of the first contemporary music composers to incorporate bird songs into his works. Our focus on music and paintings stems from their prominence in both human and non-human artistic expression, as well as their significant development within machine and AI-generated art. These art forms are not only the most extensively studied in animal behaviour research but also represent the forefront of technological advancements in creative AI applications. By concentrating on music and paintings, we aim to explore the complexities of authorship in areas where the intersection of biological and technological creativity is most evident and advanced. This deliberate choice allows us to delve deeply into the implications of authorship across these two major domains of artistic expression, providing insights that are directly relevant to ongoing discussions in both animal studies and AI research.

The definition of art frequently hinges on the concept of the creator’s intentionality, such as the formation of a goal to be realised (Beardsley, 1970 ; Levinson, 1979 ; Bloom, 1996 ). A simple question then arises: do the above animal creations fall within the realm of art? Do these animal-creators become authors of their creations or even artists? The definition of art is often introduced by the notion of intentionality of its creator, e.g., the conception of a goal to be achieved. We can thus question the levels of intentionality and consciousness of action in these animals. This reflection can also extend to machines and artificial intelligences (AIs Footnote 2 ) (Mikalonytė and Kneer, 2022 ) whose creations, sometimes indistinguishable from human ones, now fetch several hundred thousand euros (Doherty, 2019 ). In 2005, a captive chimpanzee named Barney was observed playing percussion on a plastic barrel (Dufour et al., 2015 ). An in-depth analysis of the recorded sound demonstrated rhythmicity, decontextualisation, and control of the gesture by this chimpanzee (Dufour et al., 2015 ). Prior to Barney, the bonobo Kanzi, trained in sign language, had also been observed playing percussion rhythmically (Kugler and Savage-Rumbaugh, 2002 ). Unfortunately, observations of such behaviours are extremely rare, and data are lacking.

Simultaneously, other primates and great apes draw and paint when given the opportunity (Fig. 1A ; for a review among non-human primates, see Martinet and Pelé, 2021 ). Their creations, often compared to children’s scribbles (Fig. 1B ), are exhibited, sold, or printed on accessories (Applegate and Grupper, 2013 ; Matsuzawa, 2017 ) without questioning their authorship as given to artists (Fig. 1C ). Ownership of a scarf adorned with patterns by the female chimpanzee Ai, or a painting by the male chimpanzee Congo from the 1960s, introduces a dilemma regarding authorship. If a chimpanzee can be acknowledged as the creator of its work, the question extends to why a young child’s scribbles, a worm’s paint trail, or a machine’s programmed drawing (Fig. 1D ) should not receive similar recognition. This prompts a broader enquiry into how creation is defined across early, non-human, or even non-biological contexts. It also brings the concept of intelligence into the discussion, ranging from the cognitive capabilities necessary for the intention behind creation to those required for recognising a creation as such. From this concept of ‘creative’ intelligence, arise other ethical and legal concepts, notably at the origin of the question of legal personality. This paper will address these different notions in order to discuss authorship, considered as the act of characterising a full-fledged author and recognising them as such, and the appropriation of creation, whether it is auditory or graphic, created by an animal or a machine.

This figure presents a diverse array of drawings originating from varied sources: A an adult chimpanzee, illustrating non-human animal creativity that challenges traditional notions of artistic authorship (drawing previously collected by Cédric Sueur in 2019 and extracted from Martinet et al. ( 2023 ) dataset, with the courtesy of Tetsuro Matsuzawa); B a 2-year-old human toddler, representing the nascent stages of human creativity and expression (drawing anonymously and previously collected by Marie Pelé in 2018 and extracted from Martinet et al. ( 2021 ) dataset); C an adult human professional artist (drawing anonymously and previously collected by Cédric Sueur in 2018 and extracted from Martinet et al. ( 2021 ) dataset); and D a visualisation generated by a simple random walk (Sueur, 2011 ) algorithm developed in NetLogo (Tissue and Wilensky, 2004 ), demonstrating how artificial intelligence can create patterns that mimic certain aspects of artistic creativity. Netlogo model available on Netlogo Community platform © Cédric Sueur.

Of the intention to create…

Picture an earthworm traversing a sheet of paper, trailing paint in its wake, contrasted with a chimpanzee applying paint to paper with a brush. This juxtaposition raises the issue of whether the creative processes of these two animals are comparable and if both can be deemed authors of their creations. Furthermore, it questions whether they should be regarded as equals in terms of authorship and what is behind this term. In the context of art and creativity, indexicality refers to the way in which a piece of art can act as a direct indicator or physical trace of its creator’s actions or intentions. This concept is deeply intertwined with intentionality, especially when considering the creative outputs of animals like chimpanzees and the products of artificial intelligence (AI). In animal drawings, for example, the indexicality of a chimpanzee’s artwork can be seen in the brush strokes, patterns, and choices of colour that directly relate to the animal’s physical movements and decision-making processes at the moment of creation (Martinet et al., 2021 , 2023 ). Similarly, in the realm of AI-generated art, indexicality manifests in the output of algorithms designed to create visual or auditory artworks. The ethics of virtue discussed by Aristotle or Kant (Betzler, 2008 ) focuses on the character and motivations of the individual, known as the agent, while consequentialism focuses on the consequences of the actions taken, without regard for the means employed and potential intentionality. Thus, virtue ethics allows us to consider art as the only intentional process, regardless of the final outcome. In contrast, consequentialism views art as a finished product, without considering the process of creation. Therefore, the intentionality of an animal artist or musician emerges as a key point in defining whether they are truly the author of their graphic production or musical composition. For some authors, intentionality even characterises art and the artist (Beardsley, 1970 ; Levinson, 1979 ; Bloom, 1996 ). For others, intentionality is defined by five elements: (i) a desire for a result, (ii) a belief about the action leading to that result, (iii) an intention to carry out the action, (iv) awareness of the accomplishment of the intention while executing the action, and (v) the ability to execute the action (Malle and Knobe, 1997 ).

In cognitive ethology, studies have shown that individuals of animal species such as pigeons, rats, or non-human primates are capable of anticipating their actions (Le Neindre et al., 2018 ). Individuals of most of these species are also aware of their decision-making and the consequences of these decisions. These degrees of action anticipation correspond to elements (i) and (ii) as defined by Malle and Knobe ( 1997 ), while the level of consciousness in these animals relates to elements (iii) and (iv) that define intentionality, again according to Malle and Knobe ( 1997 ). By applying these two capacities (anticipation and consciousness) to behaviours such as drawing, for example, consequentialism would qualify both the ape and the earthworm as authors of their drawings, while virtue ethics would consider the ape as the sole author, conscious of its actions. Consciousness is conceptualised as the awareness of oneself and one’s surroundings, a state that encompasses the ability to experience sensations, thoughts, and emotions. From a neuroscientific perspective, consciousness is associated with specific patterns of brain activity and connectivity that denote an awareness of internal and external states. Ethological studies further validate these concepts by demonstrating instances of self-awareness and environmental responsiveness in non-human animals, indicating levels of consciousness that challenge the boundaries traditionally reserved for humans. Philosophically, this definition is accepted as it resonates with discussions on the nature of mind, self, and agency, bridging empirical observations with theoretical inquiries into the essence of sentient life.

Nevertheless, it is necessary to determine whether these capacities are genuinely underlying animal drawing. Some researchers argue that animal drawings are induced by experimenters who invite them to draw or even encourage them to do so (see Tomasello and Call, 2004 for cognitive tasks in general). For example, in Thailand, Asian elephants ( Elephas maximus ) create shapes that closely resemble self-portraits or bouquets of flowers, but the conditioning and latent mistreatment behind such productions lead researchers to doubt the pachyderm’s understanding of its drawing (OneGreenPlanet, 2020 ). Moreover, in the wild, no spontaneous drawing behaviour has been reported to date in elephants or great apes. However, it is common for captive hominids to manipulate pencils and brushes on sheets of paper or even draw with their fingers on touchscreens (Martinet and Pelé, 2021 ). Thus, some chimpanzees maintain their graphic activity without any food reinforcement, indicating their interest in the action (Boysen et al., 1987 ). Beyond the sensation directly related to locomotor movement, visual feedback would also serve as reinforcement. Indeed, the drawing behaviour on a touchscreen decreases when the chimpanzee’s trace becomes invisible (Tanaka et al., 2003 ). The various studies on chimpanzees, therefore, support the argument that the act of drawing itself has a reinforcing property for these animals. While drawings are not spontaneous in chimpanzees, the simple act of drawing and the properties of the drawing modify the subjects’ future actions. However, if the earthworm leaves no trace behind, this absence of a trace will not impact its movements on the paper, unlike the hominid. Schiller ( 1951 ) went further and presented a young female chimpanzee named Alpha with blank sheets featuring geometric figures. She marked the sheets differently based on the stimuli presented, thus raising the question of intentionality behind these seemingly ‘simplistic’ ‘scribbles’ by animals. However, despite numerous studies on great apes, especially chimpanzees, no representative drawings have ever been observed, and researchers generally compare their productions to those of young human children (Martinet and Pelé, 2021 ). Using fractal mathematical indices to assess the representativeness of a drawing, Martinet et al. ( 2021 ) demonstrated that chimpanzee drawings, while not as proficient as those of children, are not random either. The most common way to determine if a drawing is representative or not is to ask its author about its meaning. The question was posed to another sign-language-proficient female chimpanzee, and her response was ‘bird’ (Gardner and Gardner, 1978 ). However, this response does not prove the presence of intentionality in this individual; it could have been a random response, influenced by experimenters, or the sign may have been misinterpreted. All of these elements suggest that some individuals of certain species, especially great apes, appear to interact with their production (graphic or auditory) in the sense that what is created influences what will be. The concept of agency (McFarland and Hediger, 2009 ; Blattner et al., 2020 ; Sueur et al., 2023 ) can thus be attributed to these animals from a psychological perspective, for example, as actors in the world affecting their environment. It can also be attributed to a philosophical and ethological perspective if we consider great apes as capable of recognising themselves as individuals and having a certain sense of morality if these capacities define the very essence of personhood. Legal personhood refers to the recognition by the legal system of an entity as a subject that can bear rights and duties. Traditionally reserved for human beings, the concept’s boundaries are being tested by advancements in AI and growing awareness of animal cognition and social complexity. This general definition of agency is accepted by biologists, psychologists as researchers in law and philosophers. In contemporary fields of art history, theory, and creative practices, the term ‘agency’ is employed to denote the capacity of individuals or entities to act autonomously and make independent choices within the creative process. Agency emphasises the role of the creator not just as a passive conduit for external influences but as an active participant with the power to shape the creative outcome. This perspective acknowledges the complexity of creative acts, recognising them as the result of deliberate choices, influences, and interactions between the creator’s intentions and the medium’s possibilities. A cross-disciplinary approach to agency enriches our understanding of art and creativity by challenging anthropocentric views and expanding the notion of who or what can be considered a creator. It encourages a re-evaluation of the criteria for authorship and creativity, pushing us to consider the ethical, philosophical, and practical implications of recognising agency in a broader spectrum of creative entities.

… to author recognition

Mylène Ferrand Lointier’s doctoral thesis ( 2022 ) in Arts ‘Le Tournant animal dans l’art contemporain (de 1960 à nos jours), approche écoféministe,’ [The Animal Turn in Contemporary Art (from 1960 to today), an Ecofeminist Approach] delves into the increasingly significant role of the animal question in contemporary discourse, driven by evolving anthro-zoological relationships. Highlighting a shift in the portrayal of animals in art from mere objects or symbols to subjects with intrinsic value, this research examines a diverse international corpus of artworks and artists deeply engaged with animal issues from the 1960s to the present: lEija-Liisa Ahtila, Julie Andreyev, Banksy, Joseph Beuys, Sue Coe, Minerva Cuevas, Terike Haapoja, Jonathan Horowitz, Joan Jonas, Jenny Kendler, EvaMarie Lindahl, Isabella & Tiziana Pers, Araya Rasdjarmrearnsook, Rachel Rosenthal, Saeborg, Lin May Saeed, Sin Kabeza Productions, Bryndís Snæbjörnsdóttir & Mark Wilson, Diana Thater, and Robert Zhao Renhui. Through an ecofeminist lens, incorporating ethics of care and intersectionality, Ferrand explores an ecocritical territory intertwining art, emotion, animal ethics, and posthumanism. This thesis aims to chart a new eco- and zoo-poetic/political path towards an era termed the ‘Ecocene’, advocating for a revaluation of human–animal relations within art as with The Compassion Manifesto: An Ethics for Art + Design and Animals (Andreyev, 2016 ).

There is currently no research on the abilities of great apes to claim ownership of their creations, whether they are graphic or musical. Nevertheless, it becomes evident that consciousness and morality emerge as pivotal concepts in the realm of authorship. Morality is understood as a set of principles or guidelines that govern the behaviour of individuals within a social context, reflecting notions of right and wrong, justice, empathy, and welfare. Ethologically, the roots of moral behaviour are observed in the social interactions of non-human animals (e.g., great apes, elephants or even rats), where acts of altruism, cooperation, and fairness are not uncommon and serve to maintain social cohesion and mutual benefit (De Waal, 2016 ). Such behaviours suggest a biological underpinning for moral conduct, further supported by neuroscience, which identifies neural circuits and processes involved in empathetic responses, decision-making, and the evaluation of fairness and harm. Philosophically, these empirical findings are incorporated into broader discussions about the nature of morality as well as consciousness, their origin, and their applicability across different forms of life. By acknowledging the evidence of moral-like behaviours and consciousness in non-human animals, philosophy expands its enquiry into the moral agency, questioning the exclusivity of moral consideration and rights to humans and opening the door to a more inclusive understanding of moral subjects. These concepts enable individuals to achieve the status of a ‘person’, nothing more and nothing less, and subsequently, to acquire legal rights through their recognised personhood. Historically, this term ‘person’ has been exclusively applied to humans and is defined as ‘an intelligent, thinking being, capable of reason and reflection, who can recognise themselves as the same thinking entity across different times and places’ (Locke and Perry, 1975 ). Self-awareness and moral cognition thus play a significant role in the authorisation and appropriation of creative works, attributes currently ascribed solely to humans, who are deemed to possess ‘a soul and consciousness’ (Schrecker, 1938 ; Engels, 2009 ).

It is indeed in the field of metaphysics that are often grounded the basis for the attribution of rights for living beings, and more precisely in the question of the difference between humans and animals. Pivotal moments in philosophy engage in a significant debate regarding the anthropological question and the specific traits that distinguish humans from animals. A crucial argument for this discussion revolves around whether the differentiation is grounded in ontological terms, suggesting a fundamental disparity in nature or condition between humans and animals, or if it hinges on ontic qualities, indicating inherent, distinct characteristics between the two. Through the lens of an ontological distinction, numerous philosophers have constructed a recognised hierarchy within the spectrum of living beings, often based on universals (such as soul, conscience or reason). As an example, Aristotle considers that the human soul is the only one to demonstrate the dianoetic faculty, which means the ‘power of thinking’ (Aristotle, 1987 , Book II, Chapter I, 412a) or the ability to exercise and apply reason. Descartes, by qualifying human beings as the only ones that possess the cogitatio , discloses a theory where animals are constituted as mere biological machines (Descartes, 1637/ 1937 , p. 164), providing a landing mark for later theories on the mechanism and animal environment (Loeb, 1918 ). Malebranche ( 1997 , book VI, part 2, chapter vii) argues that ‘in animals, there is neither intelligence nor souls as ordinary meant’. And later, Marx ( 2022 /1845, MEGA I, 5, p. 10; CW 5, p. 31) elaborates that ‘men can be distinguished from animals by consciousness, by religion or anything else you like’.

By those few examples, we highlight that major authors in the philosophical tradition have influenced current interdisciplinary discussions on animal rights, and the ethical and political treatment of non-human entities. Ontological attributes such as consciousness, morality, reason, intelligence and self-recognition have traditionally been used as a way to distinguish human beings from animals. The anthropological distinction between humans and animals ensues the possibility to acknowledge ontic differences and to ground them in a metaphysical perspective, giving them an axiological weight that easily leads to a hierarchy of living beings. Even though we will not tackle this issue in our article, this metaphysical debate is still widely discussed today and of major importance. But one of its many consequences is noteworthy: it impacted our ability to ground animal agency over a robust ontological status, which led to the denial of several categories of rights for animals, including the right to be acknowledged as authors (since, by definition, authorship has been attributed to individuals and people).

Even if ontological hierarchies have often been used as the groundwork on which are based distinct rights for human beings and animals, several contemporary debates explore the possibility that the possession of specific ontic, cognitive, or emotional attributes might entail the entitlement to certain rights, regardless of the inherent nature, status or condition of the subject. In short, the question of animal rights could be distinguished from the ontological question of the essence of animality, and more empirical ethological, bio-semiotical or zoological studies could lead to a better understanding of animal cognition, sociality and behaviour, leading to the establishment of grounded animal rights. From then on, numerous researchers, including Charles Darwin, who ascribed consciousness to individuals within social species, have probed the presence of intellect, self-awareness or autonomy in animals. Donald Griffin ( 2013 ) proposes that it is through the realms of communication, encompassing dialogues and negotiations, that we should investigate intentional behaviours and processes governed by consciousness. Research on great apes capable of using sign language or symbols has shown that they can speak about others and themselves as distinct and autonomous entities. Autobiographical self-awareness (Le Neindre et al., 2018 ) has also been found in many animal species (primates, cetaceans, birds) through the mirror test, demonstrating that subjects can identify and recognise themselves in it (Gallup et al., 2002 ). Likewise, metacognition, the ability by which an individual acquires knowledge of their own mental processes, or their ability to evaluate the state of what they know, has been verified in several animal species through tests assessing certainty or confidence (Le Neindre et al., 2018 ). Additionally, some social animals also display theory of mind, i.e., the capacity to impute a mental self to conspecifics or to understand what they are looking at, what they intend to do, or even to know their beliefs (Tomasello and Call, 1997 ). Finally, observations of chimpanzees and elephants, as well as experiments with rats, have shown that these animals possess a certain degree of empathy and morality (De Waal, 2006 ). From these new findings, the qualification of ‘person’ could be attributed to animals capable of self-recognition and demonstrating morality. By assigning legal personality, they could be granted ownership of their creations and recognised as genuine authors.

Legal personality

Therefore, some rights could be attributed to animals who possess capacities or qualities similar to those of human beings, beyond the ontological question of their essence, nature or condition. As highlighted by the example of marginal cases by Peter Singer and discussed by DeGrazia ( 1990 ), several categories of individuals—such as infants, adults with severe mental disabilities, or those in a coma—do not possess the cognitive, locomotor, or emotional faculties usually attributed to an ideal type of human being. Nevertheless, these individuals do have legal personality. According to Peter Singer, if these individuals have legal personality, it could also be attributed to animals who have equal or sometimes more developed capabilities than these individuals. The young child or disabled adult who scribbles or drums has rights and will be recognised as the author of the drawing or sound they produce, regardless of the levels of intentionality and consciousness they put in their creation (in the process and towards its finality). By considering the complex philosophical question of the nature of the subject as a separate discussion from the legal rules that apply to them, animals could be granted legal personality based on certain faculties they possess, allowing them to hold rights and duties. This question entails difficult debates, and several actions that aim to grant rights to animals have already been initiated by animal protection associations. In particular, in the United States of America, the Non-Human Rights Project led by Steven Wise (Wise, 2010 ) relies on the legal concept of Habeas corpus, which states the fundamental freedom not to be imprisoned without trial, and aims to free several wild animals that are held captive despite possessing enough cognitive abilities to be aware of their deplorable living conditions.

As shown with the macaque Naruto (Guadamuz, 2016 ; Rosati, 2017 ), recognising animals with consciousness as legal persons appears to be a prerequisite for them to be truly and fully recognised as authors of their creations. In 2019, the Toulon Declaration reiterated the Cambridge Declaration on Consciousness ( 2012 ), stating that most animals with neurological substrates of consciousness should have legal personality (Regad and Riot, 2019 ). The theory of animal rights is increasingly discussed and formalised, allowing animals, depending on their species, agency, and interactions with humans, to have recognised rights (Donaldson and Kymlicka, 2011 ). The authorisation of animal artistic creations fits seamlessly into this theory.

What about artificial intelligence (AI)?

When highlighting the argument of authorship for non-human beings, an important discussion arises from the question of non-living or non-organic beings, such as machines, robots and AI. The philosophical considerations surrounding the attribution of rights to animals and machines or AI overlap in significant ways. By examining these questions in parallel, we gain insights into the principles that currently guide our interactions with non-human entities, whether biological or artificial. It helps us to reconsider our relationship with non-human entities and to reassess the values and norms that underpin our social and legal systems.

As such, our demonstration will mostly focus on the question regarding AI. Indeed, a proposition would be to consider that there could be a major distinction for authorship between machines themselves because the embodiment of a robot Footnote 3 would have a huge impact on its perceived authorship compared to a non-embodied AI system. As such, a robot would be perceived as more susceptible to have rights than a non-embodied AI. In this case, embodiment itself would have a significant impact on whether or not something possesses rights and whether or not people believe that those rights are acceptable Footnote 4 . However, this thought experiment would be confronted with the fact that, currently, authorship is not attributed depending on their embodiment to living beings such as animals (which are by definition embodied). When determining authorship, embodiment appears to matter less than the legal personality we usually ascribe a being, and their status—be it human, animal, or machine, but also depending on if it is an adult or a child, or the degree of agency we ascribe to the animal in question. Therefore, we chose to mostly focus this line of questioning on IA algorithms that could or could not be embodied.

In 2018, an AI-created painting was auctioned for $432,500, signed with a mathematical formula, developed by the Obvious research collective (Doherty, 2019 ; Vernier et al., 2020a , 2020b ). This event raises the question of whether artificial intelligence could be acknowledged as the creator of its works and entitled to copyright rights. Additionally, platforms like Playform by Artrendex Inc. offer algorithms that replicate the style of renowned painters on any image. This situation poses a dilemma regarding the true authorship of the resulting artwork: Is it the AI (Davies, 2011 ; Abbott, 2016 ; Christie, 2018 ), the developers of the algorithm, or the original artists whose styles were emulated (Bridy, 2012 ; Hristov, 2016 ; McCormack et al., 2019 ), or another entity? The methodologies and debates surrounding animal intelligence and authorship could similarly be extended to the realm of artificial intelligence (Nguyen, 2019 ). In the case of the Obvious creation, the produced algorithm is not intelligent in the sense described above. It should be noted that the model used to create this painting was trained on existing paintings, which can be likened to the learning process present in humans. Capable of producing a specific act, this model is not, however, able to solve any problem, has no intentional acts, and is not self-aware. However, more complex robots capable of recognising themselves in a mirror could well be self-aware (Hart and Scassellati, 2012 ; Schneider et al., 2014 ; Meyer et al., 2020 ). New fields of research are thus created and developed to better understand the behaviour of machines and their emerging capabilities (Rahwan et al., 2019 ; Dorigo et al., 2020 ). Specific tests such as the Turing Test (Copeland, 2000 ) exist to precisely determine the capabilities of machines, especially in relation to artistic creation (Bishop and Boden, 2010 ). However, these tests are often criticised. On the one hand, because many humans do not pass them, and on the other hand, because they are conceptualised by humans with all the anthropomorphic biases that this presupposes (Sueur and Pelé, 2017 ; Sueur et al., 2020 ). However, even if some do not wish to recognise machines as artists, it is important to note the difficulty for a human to distinguish computer-generated creations from human creations (Mikalonytė and Kneer, 2022 ).

Consequently, the question of authorship and AI plays a key role in better understanding the issues of authorship and agency themselves. As regarding animals, this very specific issue depends on many factors such as the nature of the agents involved (weak AI or strong AI Footnote 5 ), the degree of intentionality of the creation (intentional or accidental), and the content or nature of the graphical creation itself (abstract or representational) (Mikalonytė and Kneer, 2022 ). However, as the attribution of agency and personhood are a crucial issue both for AI and animals, the question of authorship for machines often ends with a much clearer conclusion than that of animal authorship. Indeed, as a machine is created by humans, it is usually considered devoid of all rights in favour of its creator or user. For instance, the authorship of an AI-generated artwork is commonly attributed to the human artist behind the machine (when there is no copyrights issues…), whereas it is much more complicated to consider that the human who gave a pencil to an animal is the author of the resulting canvas. Therefore, by considering through an experiment of thought that authorship for a machine or AI is a real issue, and as it may indeed soon become a crucial one, we may better uncover what is usually the basis for authorship in a broader sense.

Photons be free

To tackle this complicated issue, we decided to incorporate and decipher a science fictional case study in our analysis. It serves as a valuable methodological tool allowing us to explore the potential futures shaped by current technological advancements and ethical considerations. Science fiction, often regarded as a form of speculative methodology, enables us to envision the societal, legal, and ethical implications of technology before they fully manifest in reality. This approach is particularly relevant when discussing the rights and authorship of non-human entities, as it provides a narrative framework to examine complex issues that might not yet be fully realised or understood within our current legal and ethical systems. To further ground our use of sci-fi examples, we draw upon the concept of technological imaginaries (Sartori and Bocca, 2023 ; Jasanoff and Kim, 2015 ), which is extensively used to describe modern societies in which technology plays a key role in shaping both our understanding of them and our way to envision their evolution. This critical theoretical framework investigates how collective visions of technological futures influence current technological development and societal norms. Technological imaginaries help us understand how our expectations, fears, and hopes for technology shape the way we interact with and conceptualise emerging technologies. By examining these imaginaries, we aim to unpack the cultural and social underpinnings of our assumptions about non-human authorship and the rights of artificial entities, offering insights that are accessible to readers from a broad range of disciplinary backgrounds. In the scope of this article, engaging with fictional narratives urges us to envision new possibilities and challenge conventional thinking about the role of machines in creative processes and intellectual endeavours, and the evolving concept of authorship in the digital age.

In this context, a specific example issued from science fiction helps us better understand this evolving aspect of our technological imaginaries and the various dimensions of authorisation that can apply to artificial or non-human entities. The episode ‘Author, Author’ ( 2001 , Season 7, Episode 20) of the series Star Trek: Voyager questions the situation of the Doctor, a hologram (with a strong AI 5 ) that has exceeded its initial programming’s limits over the years, and developed behavioural and emotional characteristics usually seen only in living beings. The Doctor is confronted with the controversial question of his own copyright when he writes a holoprogram and sells the rights to a publishing house. The editor published it without his consent while he still had major modifications to make. The stakes that this episode highlights mirror the famous debate about the rights of the android Data in the well-known episode ‘Measure of a Man’ (1989, Season 2, Episode 9) of Star Trek: The Next Generation . In both episodes, the question aims to determine to what extent Data and the Doctor are individuals because a certain number of rights will depend on their identity, status, and the legal personality that will be granted to them. If Data is not a person, then he is the property of Starfleet, much like any toaster or computer. If the Doctor is not a person, then he cannot be considered an artist and has as many rights over his holoprogram as a coffee machine has over the coffee it produces.

For the Doctor, as Data before, this debate unfolds in the legal framework of a trial whose decision will be a legal action: ‘A Federation Arbitrator has been assigned to determine whether the Doctor has the right to control his artistic creation.’ (‘Author, Author’, 00:32:13). The first session aims to outline the scope of the debate and raises the question of the Doctor’s personhood, rather than his rights as an artist. Indeed, Starfleet defines an artist as ‘a person who creates an original artistic work.’ (‘Author, Author’, 00:34:36). While the Doctor has indeed created an original artistic work, he is not considered a full person and, therefore, cannot claim authorship of his work of art.

This episode delves into a crucial aspect of our discussion: the notion that an individual must belong to a specific category to access certain rights, such as those allowing humans exclusively to be recognised as authors or artists. It proposes an alternative perspective where rights could be allocated with respect to the subject’s category, enabling entities–whether human, non-human, animal, or artificial–to be acknowledged as authors or artists. This perspective raises the possibility of recognising an animal or an AI as an artist without necessarily classifying them as persons. However, it also suggests that affirming an entity’s status as an artist might require expanding the definition of personhood. Legal decisions and efforts to establish precedents are currently addressing these considerations, bringing theoretical debates into the realm of practical applications. Last year, the United States Copyright Office (USPTO) reversed its decision to grant copyright protection to a comic book whose images were generated by AI (Ostrow and Dengel, 2022 ). The fundamental element in this latter case is that the USPTO’s revocation is based on the ‘Human Authorship Requirement’, Footnote 6 which means the necessity for the author of a work to be … a human being. Here, we find thirty years later the stake that was raised by Star Trek about the relevance of encompassing authorship in a category: be it a strictly human definition for the USPTO’s Human Authorship Requirement or, more broadly, the status of personhood or individual, in Star Trek series.

By trying to avoid the strict question of copyright and to focus on the legal personality of the Doctor, the episode ‘Author, Author’ explicitly reminds us that the question still exists today for other individuals within the human species. For example, the tendency to invisibilize female authors in artistic fields is still a contemporary issue (Rollet, 2007 ; Nochlin, 1971 / 2021 ), which questions the way we structure our understanding of creation by highlighting mechanisms that delegitimise certain individuals based on their gender or origin. This example illustrates the painstaking evolution of authorisation norms even within the human species. In this regard, the question of the right to be called an ‘author’ still arises from identity premises (the subject’s status—male or female, human or non-human, etc.) rather than from the artistic production itself. If an animal cannot be an artist, it is not because it has not created a work of art, but because it is not considered as an operating subject. And it is not the work of the non-human artificial entity that is judged, but the AI’s status within humanity, as demonstrated by the USPTO, when it first recognised the artistic value of an AI work before retracting its decision.

In the series episode ‘Author, Author’, as it is the question of personhood that is at stake through the issue of authorisation, the trial’s witnesses emphasise the importance of the Doctor’s experiences, by highlighting his ability to evolve beyond his programming, to think for himself, and even, to disobey orders. However, when he returns with his decision, the Arbitrator instructs a judgement similar to the one that granted the android Data free will in ‘Measure of a Man’, but without changing his status. The Arbitrator is not willing to declare the Doctor a ‘person’ per se. However, the legal definition of the term ‘artist’ can and will be expanded to include his creations. Therefore, the Doctor holds the copyright on his artwork and can intervene in the distribution of his holoprogram. This conclusion underlines that the status of an artist is generally deemed more flexible than the definition of personhood; and that expanding the sphere of authorship and its associated rights raises fewer challenges than to extend the concept of personhood. As such, it is easier to imagine that the USPTO could remove the Human Authorship Requirement to include non-human individuals like AI or animals in copyright protection, rather than to extend the human status to animals or AI. In other words, the legal personality is more flexible and plastic than the natural personality, understood as the nature or condition of the subject (human, non-human).

That being said, Star Trek highlights the important point that the definition of what constitutes a person itself has continued to evolve. The episode presents the important issue that conceding certain rights to certain entities (such as copyright and artistic authorship to an AI or an animal) implies, in fact, an evolution of their natural status. Moreover, these legal changes could lead to changes in ethical frameworks. In essence, granting copyright to the Doctor, then other rights to other holograms, and so on, brings closer to a legal decision in which a hologram’s personhood could no longer be questioned. To consider that personhood alone grants access to certain rights may imply that gaining these rights is the first step towards the modification of a non-human entity’s status. Star Trek offers us the following alternative: perhaps the access to certain rights should not depend on the subject’s status. Perhaps human imaginaries must reconcile with emerging realities, driven by the evolution of our technological landscapes, and acknowledge the necessity of granting rights and protections to non-human entities. In fact, it is no coincidence that the Doctor’s holoprogram, duly entitled ‘Photons Be Free’, revolves around the struggle for equality of hologram populations.

What about digital tools in musical creations?

Science fiction is often considered as a literature of ideas that allows for thought experiments in hypothetical or speculative scenarios that may not yet have real-world manifestations, but will or could have important implications for our societies. Consequently, it is now of major importance to apply the questioning that arose from this example to a more grounded debate regarding AI-generated artworks and the use of machines and algorithms in artistic practices.

Nowadays, with technological advancements and the rise of digital equipment, the issue of authorship is increasingly relevant in live performances, particularly in music (see seminal works of Reeves et al., 2005 ; Berthaut, 2015 ; Capra et al., 2020 ). The intense experience of creation ‘in the making’ can be disrupted by the presence of machines if the audience begins to believe that the machines, rather than the artists, are producing the artistic work. Unless the contributions are shared? For the artists themselves, there may be a challenge in distinguishing what is produced by their exclusive actions from what is produced by the accompanying machines (Rimoldi and Manzolli, 2016 ). Here, the question is no longer about determining whether the artist is an author or not, but to what extent, and from which perspective, that of the author or the audience.

In music, the sophistication and apparent autonomy of digital instruments raise questions that are nearly absent in the case of analogue instruments such as the violin or drums. When a machine is capable of playing autonomously—whether in a rudimentary manner, as with an MP3 player, or more adaptively, through generative algorithms and artificial intelligence–the role of the artist-author in music production no longer appears as evident, at least not exclusively. From a cognitive perspective, this dilution of the artist’s contribution due to digital assistance is partly explained by the close connection between movement and perception (Rizzolatti et al., 1996 ; Jeannerod, 2001 ). In traditional music performances where instruments have almost no autonomy, the perception of the link between the musicians’ gestures and the produced sounds is clear (these are referred to as ‘transparent instruments’ Fels et al., 2002 ). The brain’s constant simulation of perceived gestures to predict their consequences is the basis for integrating this link (Zatorre et al., 2007 ; Salimpour et al., 2015 ). From this simulation emerges the sense of understanding what is happening, perceiving what the artist controls, their intention, and virtuosity, all of which are components of authorship.

In electronic music, a single gesture can generate a multitude of different sounds depending on machine settings. AI and digital technology blur the traditional link between a musician’s actions and the resulting sounds, raising questions about authorship. The causal link between gesture and sound can thus disappear, leading to a loss of interest for the audience and doubt regarding the artist’s involvement (Schloss, 2003 ; Stuart, 2003; Huron, 2008 ). Indeed, the audience may struggle to attribute authorship due to the obscured causal relationship between gesture and sound. As AI plays a significant role in generating music, it necessitates re-evaluating how we define and recognise the creative contributions of human artists and the extent to which AI systems can be considered co-creators in the artistic process.

Towards shared authorship: from human–machine interaction to human–animal–machine collaboration?

Recent studies (Capra et al., 2020 ) have shown that the audience’s sense of understanding a digital music interaction leads them to consider the artist as more contributory than the machine in performances with digital instruments or computers. These findings not only emphasise the crucial role of the sense of understanding in the judgement of authorship but also highlight the gradual nature of this judgement. Furthermore, the ‘evidence’ of a machine’s involvement in the artistic process is not always obvious; computers can be hidden backstage, and artists can pretend to play live while everything is pre-recorded. One might have images of musicians with their hands in the air, clapping while the music continues, or, conversely, focused on their instruments without it is possible to see what they are doing or even distinguish which sound they are working on. This perceptual deficiency and its consequences on the audience’s experience have led the Human–Computer Interaction (HCI) community to propose new evaluation criteria for digital devices (Berthaut et al., 2013 ; Berthaut et al., 2015 ; Bin 2018 ; see an extended review in Capra, 2020 ), including the Association (Capra et al., 2020 ), which designates the capacity of a device to expose to the audience the respective contributions of artists and machines in electronic performances. This human–machine collaboration in artistic creation can also occur in other disciplines, such as cinema, again in various and graduated forms. In the film Attack the Sun by Gwendal Sartre and Fabien Zocco ( 2019 ), the dialogues are generated by processing content from social networks and communicated to the actors by an artificial intelligence. The artists remain in control of the overall framework and many production elements, but by letting an AI drive something as structurally significant as the dialogues, are we witnessing a strictly human production or a human–machine collaboration with a shared degree of authorship?

Thus, authorship in digital creation can be refined with more gradual notions of the level of control by the artist and the varying degree of their contribution to artistic production compared to that of machines. Additionally, as we have seen, authorship can be understood here from a dual perspective: that of the artist and their agency, and that attributed to them by the audience, referred to as attributed agency (Berthaut, 2015 ; Capra, 2020 ). This dual consideration highlights technology not only as a medium for creation but also for its mediation to make digital interactions perceptible and to reveal them (Berthaut et al., 2013 ), thus allowing authors to assert their desired level of authorship.

Delving deeper into the realm of human–animal–machine collaboration, we explore an innovative paradigm of authorship that transcends traditional species boundaries. This interdisciplinary nexus is exemplified by initiatives such as the Interspecies Internet (Dolgin, 2019 ; Jones, 2019 ) and the use of environmental sensing technologies (Gabrys and Pritchard, 2018 ), which are pioneering the way for a new form of artistic expression and communication across species and technologies. This blending of human creativity, animal behaviour, and technological intervention serves as a powerful testament to the potential of collective intelligence and creativity (Bonnet et al., 2019 ; Wang et al., 2023 ). It suggests that authorship can extend beyond the confines of human endeavour, encompassing the contributions of non-human participants whose interactions with technology provide a unique perspective to transcend creativity. The exchange of learning and behaviours between humans and animals (Sueur and Huffman, 2024 ), becomes a fundamental element of this co-creative process. This mutual adaptation and shared understanding facilitate a form of artistic creation that is truly collaborative, allowing for the emergence of novel expressions.

The outcomes of such partnerships—be it in the form of music that incorporates animal sounds interpreted through AI algorithms, or visual art that visualises the migratory patterns of birds captured via satellite technology, shapes of ants or termite colonies to co-create sculptures—enrich our artistic vocabulary.

In a mesmerising fusion of human movement and natural spectacle, choreographer Sadeck Berrabah’s Murmuration Footnote 7 stands as a profound example of interspecies inspiration in contemporary art. Drawing from the breathtaking phenomenon of murmuration—where thousands of birds, typically starlings, move in unison through the sky, creating fluid, dynamic shapes–Sadeck Berrabah captures the essence of this natural wonder through human bodies in motion. This performance blurs the lines between human and animal realms, redefining artistic authorship as a shared, interspecies endeavour.

Björk, through her innovative use of technology and nature in music, exemplifies shared authorship by integrating animal sounds and digital manipulation, blurring the lines between human, non-human, and technological creativity. Her project Biophilia Footnote 8 showcases this symbiosis, treating natural sounds not merely as inspirations but as co-creators, challenging traditional notions of creative agency. Björk’s approach, where machines serve as bridges between human creativity and the natural world, contributes to redefining authorship as a collective effort that transcends species boundaries. This perspective enriches discussions on the creative participation of non-human entities, urging a broader recognition of diverse contributions within the creative process.

Chris Jordan, Oliver Beer and Richard Mankin each uniquely engage with the natural world through their art, exploring the interplay between human activity, wildlife, and the environment. Jordan’s digital photography, especially in Midway: Message from the Gyre Footnote 9 , reveals the dire effects of plastic pollution on seabirds, offering a stark visual commentary on environmental degradation. Beer merges art with ecology, using animal sounds in installations to examine space’s acoustic qualities, connecting architectural and natural harmonies. Mankin, blending entomology with artistry, transforms insect acoustics into music, highlighting their ecological significance and challenging our perceptions of natural soundscapes. Together, these artists contribute to a broader dialogue on environmental awareness and interspecies relationships through innovative artistic practices.

In conclusion

We acknowledge the complexity inherent in discussing authorship across a spectrum of entities, ranging from animals to various forms of technology such as machines, robots, computers, and artificial intelligence (AI). We recognise that each of these entities possesses distinct levels of consciousness, intentionality, and embodiment, which significantly impact their perceived and potential authorship. To clarify, our argument is rooted in the notion that authorship should not be considered a binary attribute but rather as existing on a continuum that reflects the degree of consciousness and intentionality of the creator, whether animal or artificial. This approach allows us to critically examine the prevailing norms of personhood and human-centric authorship, while also addressing the significant impact of embodiment on the perception of authorship. Specifically, the physical presence or absence of a robot, as opposed to the disembodied nature of an AI system, influences how authorship is ascribed and perceived. We have to embrace a nuanced understanding of these differences and propose a framework for degrees of authorship, based on the capacities of both biological and technological entities. This stance not only enriches the dialogue around the intersection of ethics, law, and technology but also ensures that our discussion remains relevant and adaptable to the evolving landscape of intelligent and creative beings.

The concepts related to authorship and ownership of creation are those that define a person: a conscious entity with rights. The scientific approach to applying copyright involves various steps to assess the intentionality of an act and its awareness of it. Therefore, machines cannot currently be recognised as authors of their creations. However, if one considers that an artist is less of an author when accompanied by a machine whose contribution to the work is evident, or even superior, this illustrates the gradual nature of authorship. From the artist’s perspective, in a context where they are both the public performer and the programmer of the software used to create, they have a legitimate claim to authorship to a higher degree than if they were using prebuilt algorithms. This is a higher level of authorship than that perceived by a novice audience incapable of distinguishing the artist-computer scientist’s contribution from the presence of machines. It results in a subjective notion, nonetheless linked to objective technical knowledge of the attributed agency. In the context of collaboration between human artists and machines, and from the perspective of spectators, the notion of authorship would not necessarily imply the existence of consciousness.

However, this conclusion appears different regarding conscious animals. In the case of great apes, even though only captive individuals seem to enjoy drawing, this enculturation (Tomasello and Call, 2004 ) should not prevent us from recognising their authorship and ownership of their creations, since a similar learning process is observed in humans. Drawing or playing an instrument is a skill that develops through observation and learning in Homo sapiens , similar to other hominids, including young children who undergo a lengthy process to acquire these abilities. Mozart’s composition of musical works at the age of six illustrates that age or species does not constrain creativity. Some primatologists recognise the primates they study as co-authors in their research, publications, or productions, acknowledging their contributions (Savage-Rumbaugh et al., 2007 ; Applegate and Grupper, 2013 ; Matsuzawa, 2017 ). However, granting authorship to animals raises concerns about potentially undermining their agency (McFarland and Hediger, 2009 ; Blattner et al., 2020 ). Similarly, this article’s title, derived from the AI of ChatGPT, prompts a reflection on its authorship status. These considerations are likely to spark extensive debate within both the artistic and scientific communities in the future.

Furthermore, our exploration into the realms of authorship and creativity among non-human entities prompts a consideration of the concepts of transhumanism and transanimalism, especially in relation to the use of assisted technologies and robotics within contemporary art (Burgat, 2015 ; Delfin, 2019 ; Grundmann, 2007 ; Someşan, 2022 ; Vita‐More, 2013 ). Transhumanist and metahumanist (Sorgner and Deretic, 2015 ) movements that advocate for the evolution of the human condition through advanced technologies, offer compelling lenses through which to view the integration of AI and robotics in artistic creation. These movements question the plasticity of the human condition and envision political and practical possibilities where the boundaries between human and machine, organic and artificial, are increasingly blurred, suggesting a new paradigm of creativity that is collaborative, hybrid, and expansive in its potential. Additionally, the concept of transanimalism (Cayol et al., 2024 )—extending transhumanist ideas to include non-human animals in the technological enhancement narrative—further enriches this discourse. It invites us to reimagine the creative capacities of animals when augmented by technology, thus opening up new avenues for artistic expression that transcend traditional species boundaries. By integrating these considerations into our discussion, we acknowledge the evolving landscape of contemporary art, where assisted technologies not only redefine the parameters of human creativity but also challenge us to envisage a future where diverse forms of intelligence, both human and non-human, contribute to the tapestry of artistic expression in unprecedented ways.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

From an ethological perspective, creativity may be observed in animal behaviour that solves new problems or adapts in novel ways. In AI research, creativity is often demonstrated through the ability to produce work that is indistinguishable from or surpasses human-created art, challenging our understanding of creativity’s origins and manifestations. Philosophically, creativity involves the exploration of the bounds of imagination and the capacity for conceptual expansion, inviting a re-evaluation of creative agency across different forms of consciousness.

Just as there are multiple forms and instances of intelligence across different animal species, with varied structures and supports for intelligence, the same reasoning can apply to AI. The term ‘artificial intelligences’ acknowledges the diversity and multiplicity of AI systems, frameworks, and applications. Each AI system can be designed with unique capabilities, purposes, and underlying algorithms, thus representing distinct ‘intelligences’ in the artificial realm. This pluralisation can emphasise the variety and specificity of AI entities, recognising their individual characteristics and contributions to the broader landscape of technology and society.

A robot is typically defined as a machine that is capable of carrying out complex actions automatically, especially when programmed to do so by a computer. Not all robots necessarily incorporate AI technologies and some robots may operate based on pre-programmed instructions or simple rule-based systems without the need for sophisticated AI algorithms. But many robots can be considered embodiments of AI because they incorporate AI algorithms to interact with the physical world through sensors and actuators, process sensory information and make decisions about how to act in their environment.

The question regarding agency and IA embodiment itself is an important discussion that is notably addressed by phenomenology and cognitive phenomenology (Buongiorno, 2023 ; Corti, 2022 ; Turner, 2020 ).

Strong AI, also known as Artificial General Intelligence (AGI), refers to a type of artificial intelligence that has the ability to understand, learn, and apply its intelligence to solve any problem, similarly to how a human would. It possesses the capacity for conscious thought, understanding, judgement and self-awareness, allowing it to perform tasks requiring human-like cognitive abilities and to adapt to new situations without human intervention. Strong AI aims to replicate the multifaceted intelligence of human beings but is still a theoretical object that only exists in cultural items such as movies or video games, with famous characters such as Data ( Star Trek: The Next Generation ). Weak AI, also known as Narrow AI, is designed and trained for a specific task as or a simple computer, an articulated robot or a machine. Unlike Strong AI, it operates under a limited pre-defined range or context and does not possess consciousness or self-awareness. Weak AI is focused on executing specific applications such as voice recognition, image analysis, or executing specific functions within a software environment. Examples include virtual assistants, chatbots, and recommendation systems. While it can exhibit some level of learning and adaptation within its narrow domain, it does not have the capability to generalise its intelligence to the broad spectrum of tasks that a human or Strong AI can perform.

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Sueur, C., Lombard, J., Capra, O. et al. Exploration of the creative processes in animals, robots, and AI: who holds the authorship?. Humanit Soc Sci Commun 11 , 611 (2024). https://doi.org/10.1057/s41599-024-03125-y

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Article Contents

Introduction, the need for replication, common types of replications, self-replications versus independent replications, toward an integration of replication into research practice, general discussion, data collection statement, author notes.

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Taking the Full Measure: Integrating Replication into Research Practice to Assess Generalizability

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Oleg Urminsky, Berkeley J Dietvorst, Taking the Full Measure: Integrating Replication into Research Practice to Assess Generalizability, Journal of Consumer Research , Volume 51, Issue 1, June 2024, Pages 157–168, https://doi.org/10.1093/jcr/ucae007

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In this article, we review the ways in which replication has been and could be featured in consumer behavior, using Journal of Consumer Research as a specific setting. We present a framework for thinking about the generalizability of research findings and differentiate various potential benefits that replication can have for understanding variability in consumer research findings. We then define four different types of replications, describe how researchers can use these approaches to produce distinct benefits, and give guidance regarding conducting, interpreting, and the potential contributions of these different types of replications. We conclude with a discussion of various ways in which replication could be more fully integrated into different phases of the scientific research process, taking into account the contribution necessary for publication. In particular, we identify opportunities to incorporate independent replication into original papers, to increase the replication-based contribution in papers that build on prior work, and to use systematic replication in conjunction with meta-analysis to synthesize and confirm conclusions from a mature research literature. More fully integrating replication into scientific practice can yield a new equilibrium, in which replication is routine, typically consistent with previous results, and recognized as necessary for establishing an empirical generalization.

“The ability to systematically replicate research findings is a fundamental feature of the scientific process. Indeed, the idea that observations can be recreated and verified by independent sources is usually seen as a bright line of demarcation that separates science from non-science .” ( Dunlap 1926 )

A scientific finding is replicable if new relevant empirical evidence consistently yields a comparable result, consistent with the empirical predictions of a theory. Users of research count on it being replicable, and replicability is a key criterion for transforming data into evidence that can serve as a foundation upon which scientific knowledge is built. For research to have real-world impact also relies on replicability: developing reliable advice, applications, and policies requires knowing which findings consistently replicate, and under which conditions.

For these reasons, replicability is an ongoing concern for any science that aims to be valid, self-correcting, and practically relevant. Because replication cannot be assumed, a majority of highly cited papers in medicine, for example, are the subject of future replication tests ( Ioannidis 2005) . After all, the more useful a finding is to its field, in either practical or theoretical terms, the more important it is to be sure of its validity and generalizability, and the more value there should be in testing replication, in some form.

Discussion of replication in consumer behavior research dates back to the year in which the Journal of Consumer Research ( JCR ) was founded ( Jacoby 1976 ) and has become increasingly common since then. In particular, JCR has periodically featured calls for more replication ( Helgeson et al. 1984 ; Monroe 1992a , 1992b ; Hunter 2001 ; Mick 2003 ; Rapp and Hill 2015 ), and specific editorial initiatives over the years have attempted to encourage more replication research in JCR ( Bettman and Kassarjian 1982 ; Monroe 1992a , 1992b ; Mick 2001 ).

To better understand the nature of replication in consumer research, we conducted a systematic analysis of all papers published in JCR since its founding. 1 Out of 2664 articles, 1294 (49%) had some discussion of replication. As shown in figure 1 (solid gray line), mentions of replication in JCR have been increasing over time ( r = 0.84, p < .001). This increase in mentions of replication over time is primarily due to an increase in within-paper replications of findings ( r = 0.92, p < .001). While rare in the early years of JCR , at around 9% of papers, self-replication has now become a norm, with around 54% of papers (and a higher proportion of empirical papers) discussing replications of their own results. By contrast, reported replication of other papers has remained relatively low over time (mean = 5%, r = −0.04, p = .80). General discussions of replication, while somewhat more common, have also remained relatively constant (mean = 21%, r = 0.19, p = .19; see the web appendix for more details).

OVERALL AND SPECIFIC CHANGES IN JCR REPLICATION MENTIONS

Note.— All replication mentions (gray) are broken out into self-replications, primarily within the same paper (black), independent replications of prior findings (dashed), and non-empirical discussion of replication (dotted).

These macro trends illustrate that the concept of replication has long been present and is increasing and pervasive in discussions within consumer research and in JCR . In fact, as shown in the web appendix , JCR has both historically and recently (e.g., in 2022) had a high rate of discussing replication in some form, even compared to other journals in marketing and related fields. In this article, we attempt to provide more clarity on the use of replication, distinguishing the different methods, benefits, and interpretations of distinct types of replications. First, we describe a framework differentiating potential benefits that replication can have for consumer research. We then define four different types of replications, describe how researchers can use these approaches to produce distinct benefits, and give guidance regarding best practices for, implications of, and the potential contributions of these different types of replications. We conclude by discussing how to evaluate the contribution to research provided by different replication approaches and strategies for integrating replication throughout research practice.

This model is empirically testable by measuring X and Y or by manipulating X and observing the resulting differences in Y . The error term e means that we cannot expect a change in X to result in the same change in Y every time. Instead, b represents an average estimated effect corresponding to a specific theoretical prediction or implication, rather than a deterministic effect of X on Y . The convention of treating e as random error is an assumption of convenience. By definition, e represents any deviation of actual values of Y from the corresponding predicted value ( a + b  ×  X ), which includes all omitted other causes of Y that may vary in the world.

This formulation represents a hierarchical linear model, in which the estimated coefficient of interest is decomposed into various additive factors that cause (or predict) variation in the coefficient ( Yarkoni 2022 ).

Equation 2 is just one possible representation, specifying common types of variables that the coefficient may vary across, and excluding any interactions. Each b k may be a vector of coefficients, corresponding to a set of predictor variables of a given type. In this model, Sa denotes the sample of participants, P is for the population the sample was drawn from, T is for time-varying factors, C is for contextual factors, St is for the stimuli, O is for how the outcome (i.e., the dependent variable) was operationalized and measured, I is for how the intervention or predictor (i.e., the independent variable) was implemented, E is for the estimation method, and R is for researcher-specific factors.

The fundamental motivation for replication is that any test will only yield one specific estimate of b , which will depend on the values of Sa, P, T, C, St, O, I, E, and R. However, consumers of research are often interested in either the average effect of X on Y across some representative set of circumstances, or the expected effect of X on Y in a particular circumstance. Thus, the need for replication arises from the potential heterogeneity in b , which calls into question the validity and generalizability of a specific finding ( Lynch 1982 ; Monroe 1992a ; Wells 2001 ; LeBel et al. 2017 ; Fabrigar, Wegener and Petty 2020 ; Strømland 2021 ). When a replication varies one or more of the relevant factors that plausibly impact b , it can inform which realization of b interested parties should expect in a specific setting. As a result, differences across replications that vary relevant factors (as opposed to identical replications) can provide information about how b may vary with the factors that differed.

Next, we describe distinct types of replications in terms of which factors (e.g., Sa, P, T, C, St, O, I, E, and/or R) are varied in the replication and which are held constant. We describe what each type of replication can be used for, suggest best practices for conducting each type of replication, and discuss the interpretation of results from each type of replication.

In this section, we describe four distinct and commonly used approaches to replicating a specific test (as opposed to conceptual replications, Zwaan et al. 2018 ): exact, study-design, methodological, and theory-test replications. Our goal is to provide detailed descriptions capturing useful distinctions that may be overlooked in more commonly used terms (e.g., direct vs. conceptual replications). Of course, these four types of replications do not represent a collectively exhaustive list. We focus on these types of replications to provide a conceptual continuum, and to highlight the differences in methods, the unique benefits that each offers, and the different implications of potential results. We reserve discussion of researcher-specific effects (R) for the following section.

The four types of replications are shown in table 1 , ordered from left to right based on the intended similarity to the original study, with exact replications being the most similar and theory-test replications being (potentially) the most different. Further, the replications that appear earlier in the list are largely nested within those that appear later. For example, each exact replication is a study-design replication, methodological replication, and theory-test replication; however, a theory-test replication is not necessarily a study-design replication. In figures 2 – 5 , we provide concrete examples describing how a hypothetical researcher named Avery who is investigating choice overload (whether providing a decision maker with more options can be detrimental to choice; Chernev, Böckenholt, and Goodman, 2015 ) could employ each type of replication.

EXACT REPLICATION EXAMPLE

TYPES OF REPLICATIONS

Exact Replication

An exact replication involves testing a specific result under exactly the same conditions as the original investigation. Exact replications only differ from the original investigation by the particular sample of participants or data (Sa) included in the study [i.e., all other factors, including the population (P), stimuli (St), and contextual factors (C), are held constant]. Because only the sample differs from the focal study, the exact replication tests for variation from the original finding due to sampling error or practices that may capitalize on chance.

One example of an exact replication is cross-validation using a holdout sample: randomly selecting cases from the full dataset that are then used to test the validity of an analysis (typically model-fitting) that has been conducted on the rest of the data. In general, for work using observational data, exact replications are an invaluable tool to address concerns about results being due to random chance ( Jacoby 1976 ), particularly when the analysis involves meaningful researcher flexibility. An exact replication can be particularly useful when conducting exploratory research, especially in circumstances that preclude later replication (e.g., a study conducted in the context of a new product launch).

For experimental work, the only way to create an exact replication is to simultaneously randomly assign participants to an “original study” and “replication study”. Otherwise, it is impossible to be certain that time-varying and population factors did not differ across studies, even when using exactly the same methods. Given these stringent requirements, true exact replications of experiments are rare ( Zwaan et al. 2018 ). Fortunately, there are other, more practical, ways to address robustness to random chance (e.g., detailed preregistration of the analysis plan). Furthermore, often the concern is whether the finding is robust to variation in specifically theory-irrelevant factors, which can be addressed by study-design replications, as discussed next.

Exact replications are expected to generate similar results. When an exact replication does yield a similar result, we have stronger evidence and a more precise estimate of b under the same specific factors. However, findings that are not consistent (i.e., meaningfully differing in effect size, not just in significance testing) under exact replication may be spurious—they may not provide an accurate estimate of b even when all possible factors are held constant. A different result in an exact replication requires updating beliefs about the magnitude of b from the original study (e.g., based on all the data) and could suggest that the procedures involved in generating the original estimate inflated b by capitalizing on chance.

Study-Design Replication

Study-design replications retain all aspects of the original design judged to be theoretically relevant. However, such attempts to replicate prior findings are not exact replications, because they do vary one or more factors, typically out of logistical necessity, that are assumed to not matter for the finding (i.e., for the estimate of b ). For example, the replication may sample from the same population (P; e.g., MTurk participants) but at a later time, or may use a different population that is assumed to be comparable (e.g., undergraduate participants, but at a different institution from the original study), or may involve different contextual factors (C; e.g., an in-person study instead of online). Study-design replications are valuable because they are necessary to obtain a precise estimate of b in a particular set of circumstances and speak to the robustness of a particular finding to those factors assumed to be irrelevant .

Study-design replications that produce results consistent with the original work provide evidence that a finding replicates in similar circumstances, increasing confidence that the finding can be applied in subsequent research or practical use. However, a study-design replication that does not produce results consistent with the original work calls into question whether the original study only provided narrow circumstance-specific evidence for the relevant theory, or was spurious, if actual heterogeneity was low ( Coppock, Leeper, and Mullinix 2018 ). This could either mean that seemingly unimportant factors actually represent missing boundary conditions, or that the theory is more generally not supported.

Researchers conducting a study-design replication should clearly specify which factors differ and which remain the same as the original study, because value comes from understanding and interpreting the different conditions generating the new estimate of b . For example, a failure to replicate a study with a different population may be a major challenge to the generalizability of an original theory if it did not posit population-specific mechanisms, or it could be reasonably expected if the characteristics of that population differ in a way theorized to affect the relevant behavior. Thorough and transparent reporting (e.g., posting preregistrations, study materials, and raw data with analysis code) in original research can help to enable the accuracy of future study-design replications. Researchers conducting study-design replications should test any manipulation checks or other measures of necessary pre-conditions, to distinguish between a study that fails to replicate the conditions needed to test a theory versus a successful test of theory that did not replicate the predicted result.

For study-design replications, the two factors that necessarily differ from the original study are the sample (Sa) and time-varying factors (T, representing factors that may have changed in the world since the original study took place). Theories in consumer behavior are generally not intended to be specific to a particular set of participants or a particular point in time, as a theory that only applied to a single sample or single point in time would often be of little value, either theoretically or practically. Thus, consumers of research will typically expect results of a study to be robust to variation in these factors. Differing results under different time-varying factors or with a different sample should typically be interpreted as a serious challenge to the findings from the original study.

However, there can be rare exceptions. It is important for the researchers conducting a replication to contemplate whether any differences in time-varying factors, sample characteristics, or other factors provide a theoretical or logistical reason for the result to differ. For example, if recent world events have changed the way that participants interpret the stimuli of a study or if a stimulus was chosen for specific perceptions at the time of the study (i.e., a newly released product), a later study-design replication may no longer provide a valid test of the relevant theory. Ideally, the original research would have clearly specified such necessary conditions and included empirical tests that could then be confirmed when conducting a replication, to avoid post hoc speculation.

STUDY-DESIGN REPLICATION EXAMPLE

Methodological Replication

Methodological replications test the robustness of the methods in an original study (e.g., the intervention used) to changes in other factors. Unlike study replications, methodological replications may include major changes to the study, like updated stimuli (St) or different outcome measures (O). In particular, methodological replications may focus more on validation measures, such as manipulation checks or scale validation tests, than on the original outcome. The results of methodological replications speak to whether an intervention, scale, or other methodology performs as it was proposed to in the original research.

As is the case for study-design replications, researchers conducting methodological replications should describe what factors they changed from the original study, because a contribution of the replication is to test the generalizability of the method to those changes. Methodological replications that are consistent with past findings provide some evidence that the method can reliably be used in a new research or applied setting (i.e., that the interpretation of b when using the method is relatively robust to the particular changes in Sa, T, St, Me, P, C, and/or E). Failures to replicate suggest that the effects of an intervention or interpretation of a measurement method may be more variable than originally believed (i.e., the meaning of b may depend on factors specific to the original study).

Methodological replications have clear practical implications for researchers and practitioners who are considering adopting a method from past work. Original research that introduces a new intervention, for example, makes a potential methodological contribution by creating a tool that others can employ. However, the extent of this contribution depends on whether the intervention consistently produces similar effects in different circumstances. Thus, methodological replications are important for determining whether others can count on a method working as intended.

METHODOLOGICAL REPLICATION EXAMPLE

Theory-Test Replication

Theory-test replications implement a new test of the same theoretical implication or prediction (i.e., the same b ) as the original study, potentially changing multiple factors (Sa, P, T, C, St, O, I, E, and/or R) relative to the original. Importantly, a theory-test replication is distinct from a study that tests a different theoretical implication or prediction from the original study, which, while informative about the theory, is not a replication of the original study, because it estimates an entirely different b .

The benefit of theory-test replications is that testing the same prediction of a theory using a completely new methodology provides substantial variation in the potential influences on b , such that a replication consistent with previous results may provide strong evidence of generalizability ( Blanchard et al., 2022 ). However, the results should be interpreted with caution. The more differences there are from the original study, the greater the potential concern that what seems like a replication may in fact instead be a similar-seeming effect that is due to different causes than the original.

Furthermore, when many factors vary simultaneously, a failure to replicate does not identify specific limits on generalizability, as the researcher will not have strong evidence that the difference in results is attributable to any particular manipulated factor. Thus, while theory testing replications that are consistent with previous results may increase one’s confidence that the relevant theory will hold in untested circumstances, failures often suggest that more work is needed to understand exactly what factors the relevant theory is contingent on, to refine relevant theory. For this reason, a series of theory-testing replications, each varying one factor that would be relevant for drawing conclusions about generalizability, may be most informative.

THEORY-TEST REPLICATION EXAMPLE

We can relate these four types of replications to some commonly used terms. Exact replications are sometimes called out-of-sample tests. Study-design replications are sometimes called direct replications (although that term is not well-defined) or, incorrectly in our view, exact replications. Methodological replications may be referred to as method validation. Finally, theory-test replications have been referred to as conceptual replications ( Lynch et al. 2015 ), although that term has also sometimes been used to refer to testing different implications of the same theory, which we do not consider to be replications. We have developed the present model-based taxonomy of replication types to reduce the potential for confusion, and to create terminology that we hope will increase the precision and constructiveness of conversations about replication, by being clearer about the mapping between the theory, methods, and claims in an original paper and a particular replication.

Finally, we note that researcher-specific factors (R) add another dimension to the distinctions between replications that we have discussed so far. For example, study-design, methodological, and theory-testing replications can be conducted by some or all of the same author(s) that produced the original work (self-replications) or by independent researchers who were not involved in the original work (independent replications).

Assessing the sensitivity of b to researcher-specific factors is particularly relevant to debates about replication. In the scientific ideal, the results of a replication do not depend on the particular set of researchers conducting it (i.e., b R = 0). Specifically, if all relevant research design, implementation, and analysis factors have been accounted for, researcher characteristics should have no remaining effect. In practice, however, there could be a systematic researcher-specific effect (i.e., b R may be nonzero) due to omitted factors that differ by researcher. For example, there may be unspecified boundary conditions that only the original authors are aware of, idiosyncratic research practices that are specific to a particular researcher (e.g., use of a particular attention check, or a particular data cleaning practice), or even research practices that inflate false-positive rates ( Simmons et al. 2011 ; John et al. 2012 ). “Open science” practices, such as sufficiently detailed and publicly posted preregistrations and shared stimuli and analysis code, are beneficial in this regard, by enabling the field to identify the specific conditions under which a given result was obtained, which would otherwise constitute unknown researcher-specific effects.

The potential for researcher-specific variation in b constitutes a limitation of relying primarily on self-replication. Because self-replications keep R fixed, they cannot identify researcher effects. In addition, researchers advancing a particular theoretical viewpoint (and perhaps trying to convince a skeptical review team) may, intentionally or not, skew their choice of research design factors [e.g., population (P), stimuli (St), etc.] toward those under which the effect of interest is more likely to be observed. Sometimes, as when the stated research goal is an “existence proof,” this may be directly discussed in the article, while in other cases the non-representativeness may not be acknowledged.

When an independent replication is consistent with previous results, it suggests that the methodology used in the original research is sufficiently complete and robust to be employed by different researchers or practitioners (i.e., when researcher-specific factors vary). As a result, marketers, consumers, and policy-makers should be more confident when applying research that has been replicated independently. Likewise, other researchers may be more confident in developing projects that build on research that has already been replicated independently.

As discussed earlier, our review finds that the rate of JCR papers presenting specifically independent replication is low: historically, only 5% of papers, compared to the 36% of papers that discuss replicating their own results. These data suggest that, intentionally or not, the field of consumer behavior has addressed validity concerns involving replication primarily through self-replication, typically within the original paper. In fact, it is noteworthy that the current rate of independent replication is lower in JCR than in some comparable journals (e.g., 3% of replication mentions in JCR in 2022, compared to 40% in the Journal of Experimental Psychology: General ; web appendix ).

Current standards for publication increasingly dictate that the “replication and systematic search for range and limits” stage of research development ( Wells 1993 ) take place within an original paper. Presenting replication evidence alongside the proposed theoretical advance can be a faster, more standardized, and more efficient alternative to relying on independent replication. However, even extensive self-replication in original papers cannot address the potential for researcher-specific effects. Evidence from multi-site preregistered replications of highly cited findings in the behavioral sciences ( Klein et al. 2018 ) suggest that researcher effects can exist. Thus, for all its benefits, self-replication alone seems unlikely to fully address issues of robustness and generalizability.

In order for diverse replications that can more fully assess generalizability to become more prevalent in consumer research, the field needs to grapple with the practical need to evaluate the contribution of a replication. Advocates of more replications, including specifically in JCR , have proposed criteria for assessing the contribution of a project centered around replication, such as the importance of the phenomenon, the novelty and influence of the specific paper being replicated, and the reasons for suspecting lack of generalizability ( Monroe 1992a , 1992b ; Mick 2001 ; Lynch et al. 2015 ). Below, we build on these discussions to propose specific ways for authors to incorporate different types of replications into their original research, throughout the scientific process, while considering the need for original research to meet the bar in terms of contribution.

Within-Paper Replication

The current prevalence of self-replication (typically within a single paper) in consumer behavior research is informative for assessing generalizability and is a clear indication of the field’s concern with validity. The inherent limitation of such replications is that they cannot fully address generalizability to researcher-specific factors (R). However, authors can bolster the potential informativeness of their self-replications by providing full transparency about potential research-specific factors (e.g., by sharing detailed preregistration of procedures, posting all replication data and materials, and reporting all replication attempts, including inconsistent results). These practices can be beneficial when conducting self-replications because they reduce the number of undisclosed researcher-specific effects within each study and disclose the breadth of generalizability tested (along with any discovered limitations) across studies. For replication within an original paper to serve as the primary mode of assessing generalizability, the comprehensiveness and credibility of self-replication during the review process would require the same scrutiny typically applied to questions of internal validity (e.g., confounds in experimental design) and construct/argument validity (e.g., theoretical development).

To specifically address the potential for researcher-specific factors, papers could go beyond self-replication. In much the same way that researchers often rely on independent coders unaware of the hypotheses, papers could include a study-design replication conducted by an independent replicator who is indifferent to the results, which could also be a useful tool for addressing reviewer skepticism. We are currently developing a pilot program to conduct independent replications for authors who wish to include such a study in their paper (see www.indeprep.org and p. 8 of the web appendix for details).

Independent Replication Commentaries

Some independent study-design replications of previously published results will be small in scope, empirically and/or theoretically, relative to the contribution of the original paper being replicated and may therefore not meet the contribution standards for a full research paper. Self-publication (e.g., blog posts) can miss the benefits of peer review, potentially makes the replications more difficult to find (particularly in the long term), and may be more easily dismissed by the field. The field may be best served by publishing such replications using an alternative format, such as brief commentaries or letters, as has been done recently in Psychological Science ( Bauer 2021) and previously in JCR ( Bettman and Kassarjian 1982 ; Mick 2001 ).

However, such replications should not be automatically assumed to make less of a contribution than original work. Replication-based papers that are both rigorously conducted and sufficiently ambitious, theoretically and empirically, to be informative regarding an important question can provide as much or more contribution than a more typical empirical paper, whether providing “good” or “bad” news. This may involve broadening the focus from a single finding or paper to a reassessment of the full literature that bears on the broader motivating question (e.g., by conducting an “empirical audit” on that topic, O’Donnell et al. 2021 ).

Replication during Subsequent Theory Development

In many cases, a more direct path for replication-related work to make a sufficient contribution will be to go beyond retesting a specific finding to conducting theory-test or methodological replications that have broader theoretical or practical contributions. A paper could “replicate and extend/moderate” prior findings, not only confirming but also providing a more detailed understanding. Many papers already do this, often focusing on the extension or moderation as the key contribution but sometimes failing to highlight the replication aspect and to convey what has been learned about generalizability. Alternatively, a “non-replication and explanation” would demonstrate a failure to replicate, identify reasons for the differing results (often factors previously assumed to be irrelevant), and update the theory accordingly.

Like any cumulative research, these approaches risk being perceived as making an “insufficient contribution”, particularly if the original paper already provided substantial evidence regarding generalizability. Authors need to make the case for the importance of the theory being refined and for the informativeness of the new evidence. This may be accomplished either by challenging the conclusions of the original paper or by reducing substantial uncertainty regarding the evidence in original paper (e.g., providing a more precise estimate of b ).

Replication While Testing Other Hypotheses

Research on a novel hypothesis often contains studies that are, in part, study-design or theory-test replications of the prior work that the paper builds on. Similarly, studies that use previously developed methods in new ways may constitute methodological replications, if designed to validate the method. Such studies could be designed with comparability to prior research and the resulting replication-based secondary contribution in mind. The replication contribution would also be bolstered by explicitly mentioning and sharing all replication-relevant data, with relevant citations, regardless of results. A brief summary of the replication-based implications, both positive and negative, of the paper’s findings, in the GD or appendix, would inform the field about the generality of prior research ( Chen et al. 2023 ). Far more independent replication is likely being conducted than what is apparent from reading published papers.

However, it is important to acknowledge potential disincentives to authors of doing this, particularly the risk of alienating authors whose findings may not have replicated. To generate a meaningfully representative dataset of independent replications from original research (vs. only replications with consistent results) requires that reviewers and other stakeholders recognize the value of and promote the practice of fully reporting all replication-relevant results.

Research Synthesis through Meta-Analytic Replication

Given that the benefits of theory-testing replication are primarily in assessing generalizability, the greatest need for systematic replication of these types may, in fact, come late in the research process. Science is typically cumulative, with general conclusions derived from comparison and synthesis of all the findings in a literature, in the form of qualitative (theoretical) or quantitative (meta-analytic) reviews. However, even a mature research literature may not contain sufficient, and sufficiently varied, replications to reach conclusions about empirical generalizations.

This can be remedied by making replication a key element of conducting research synthesis. Once a field is sufficiently developed for a meta-analysis on a particular question, the criteria for replication research to make a sufficient contribution are likely to have been met (i.e., the phenomenon has been deemed important, the prior work is novel and influential, and conflicting results or moderators identified in the prior work make the case that replication is needed). Preregistered, sufficiently powered, and independently conducted confirmatory replications of the meta-analytic conclusions would provide the kind of unbiased estimates that typically cannot be assumed from traditional meta-analyses alone ( Kvarven et al. 2020 ). Meta-analytic replication has been used to form conclusions about ego-depletion ( Vohs et al. 2021 ), relationships between emotion and financial decisions ( Pertl et al. 2024 ), and the effects of pain of paying ( Bechler et al. 2023) .

Ideally, replication of one’s own and others’ findings would be a standard and unremarkable aspect of conducting research. In practice, frictions and disincentives can limit the amount and types of replication findings that make it into the literature, distorting the evidentiary basis upon which consumers of research rely. When independent replication is rare, non-replication of a finding may be seen as severe criticism (as opposed to just more data) or even as a tacit accusation of fraud, raising the stakes for reporting incidental non-replication findings.

When a field lacks consensus regarding the benefits of replication in general and how to assess the contribution of a specific replication, attempts to publish replications may be especially uncertain, costly, and frustrating, influencing what researchers with scarce resources prioritize ( Romero, 2018 ). In this light, meta-scientific findings of non-replication ( Klein et al. 2018 ) and perceptions of replication studies as “policing” are a symptom that a field has insufficiently fostered a culture of self-correcting science, with distinct negative consequences.

In this article, we have attempted to promote the practice of replication by reducing some of these frictions for authors. We attempted to formally define the benefits of replication in a way that integrates the conduct and assessment of replication into the basic practice of theoretical and practical research. We then drew distinctions between common types of replications that involve different goals and correspondingly different methods, to provide a common vocabulary and shared guidelines for researchers. We describe the different ways in which incorporating replication can bolster the contribution of a specific research paper and outline specific types of papers that can be developed in line with each path to contribution. In sum, we discuss how to use replication to contribute to theory-driven empirical research, by testing generalizability and thereby providing the evidence for theory confirmation or disconfirmation, theory extension, or theory integration and expansion.

Given the costs of producing empirical papers, it is important for replications to be integrated throughout existing research practices, rather than constituting additional costs. Our framework suggests ways that authors can execute and communicate the study-design, theory-test, and methodological replications that are already being conducted as part of original research. We are hopeful that authors can use independent replication to address some reviewer concerns more effectively than running additional original studies. We give suggestions outlining how authors of replication-based papers may achieve and communicate sufficient contribution. Finally, while meta-analytic replication would involve collecting additional data, this may be effective as a follow-up paper to a high-contribution meta-analysis paper, or as an approach that can complete a meta-analysis paper that would otherwise have an insufficient contribution.

Replicability enables consumer research to build on prior findings by identifying substantiated theories and validated methods and to identify promising new questions and provide reliable guidance to consumers, practitioners, and policy-makers. The dramatic increase in self-replication, seen in the scope and content of JCR articles over the past 50 years, indicates a concern for replicability. More fully incorporating a variety of replication approaches (including independent and meta-analytic confirmatory replication) throughout the research process would further improve the validity of consumer research. Integrating replication throughout scientific practice can yield a new equilibrium, in which replication is routine, typically consistent with previous results, and a prerequisite for establishing an empirical generalization.

The first author collected the data on prior publications along with research assistants at the University of Chicago in the summer of 2022, and updated them in the winter of 2022 and summer of 2023. The first author analyzed the data. The data files and study materials are available at https://osf.io/my2d3 .

Oleg Urminsky ( [email protected] ) is professor of marketing at the University of Chicago Booth School of Business, Chicago, IL 60637, USA.

Berkeley J. Dietvorst ( [email protected] ) is associate professor of marketing at the University of Chicago Booth School of Business, Chicago, IL 60637, USA.

Both authors contributed equally to this article. The authors thank Anastasiya Apalkova and Jieyi Chen for research assistance. Funding for research assistance was provided by the Dean’s Office of the University of Chicago Booth School of Business to the first author. Supplementary materials are included in the web appendix accompanying the online version of this article and at https://osf.io/my2d3 .

Additional details and data are publicly available at https://osf.io/my2d3 .

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• Open access
• Published: 09 May 2024

A systematic review of telemedicine for neuromuscular diseases: components and determinants of practice

• Deniz Senyel 1 , 2 ,
• Katja Senn 1 ,
• James Boyd 2 &
• Klaus Nagels 1  

BMC Digital Health volume  2 , Article number:  17 ( 2024 ) Cite this article

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Introduction

Neuromuscular diseases (NMDs) entail a group of mostly inherited genetic disorders with heterogeneous phenotypes impacting muscles, the central or peripheral nervous system. They can lead to severe disabilities and shortened lifespans. Despite their severity, NMDs often lack in public awareness and appropriate medical and social support. Telemedicine can improve patients’ and caregivers’ lives by enhancing continuity of and access to care. The first aim of this systematic review was to summarise the status quo of telemedicine services for patients with NMDs. Secondly, barriers and facilitators of the respective implementation processes should be analysed.

The databases PubMed, Web of Science and CENTRAL by Cochrane were searched in May 2022. To be truly explorative, any original evidence from any setting was included. Two independent researchers completed the screening process. Data was extracted and analysed using the taxonomy of Bashshur et al. (2011) and the Consolidated Framework for Implementation Research (CFIR).

Fifty-seven original papers were included in the systematic review. The results showed a high representation of teleconsultations and remote monitoring studies. Teleconsultations replaced in person appointments and telemonitoring mostly focused on ventilation. Physical therapy, pulmonology, neurology, and psychology were the most represented medical specialties. We found barriers and facilitators relating to implementation mainly referred to the intervention and the individuals involved. Technical errors and inaccessibility due to a lack of technical devices or the patient’s disability were stated as hindrances. A positive mindset of users as well as patient empowerment were necessary for the adoption of new technology. Technophobia or uncertainty around technology negatively impacted the implementation process.

This systematic review provides an overview of the current use of telemedicine in patients with NMDs. The distribution of telemedicine interventions between the defined domains was very heterogenous. Previous research has neglected to fully describe the implementation process of telemedicine for NMDs.

The evidence shows that telemedicine can benefit patients with NMDs in a multitude of ways. Therefore, health policies should endorse and incentivise the uptake of telemedicine by institutions and health care workers. Further research needs to be conducted to confirm the current evidence and close existing research gaps.

Peer Review reports

Neuromuscular diseases (NMDs) are a heterogeneous group of disorders, that affect the nerves controlling muscles, leading to muscle weakness, wasting, and other related symptoms [ 1 ]. NMDs are often hereditary and have been linked to 500 different affected genes [ 2 , 3 ]. Most NMDs are classified as rare diseases. The prevalence of NMDs can vary widely and, even for common diagnostic groups, the prevalence ranges between 0.1 to 60 per 100,000 [ 4 ]. The onset, cause, and course of the disease vary widely between disorders [ 5 ]. While each individual's experience is unique, there are common disability-related challenges faced by patients with NMDs. Acknowledging these commonalities and addressing the unique needs of each person are essential for providing comprehensive care and support to individuals and their families living with NMDs. NMDs are highly complex diseases defined by a degenerative course and progressive muscle weakness as the main symptom. Their impact extends beyond the musculoskeletal system, affecting various organs and systems throughout the body, such as eyes, lungs or the brain [ 1 , 2 ]. As a result, patients suffer from a reduced quality of life and a significant disease burden [ 2 , 6 ]. Multidisciplinary care is often considered the optimal approach for providing holistic treatment and symptomatic management for individuals with NMDs [ 7 , 8 , 9 , 10 , 11 ]. The needs of patients during disease progression are ever changing based on disease stage, symptom burden, and personal priorities. General practitioners, specialists, and allied health professionals each bring unique expertise to the care team, allowing for comprehensive, patient-centred care that adapts to changing needs and priorities throughout the course of the disease and ensures continuity and quality of care [ 1 , 12 , 13 ]. Recognising and supporting caregivers is crucial in the care of NMD patients. Most NMD patients receive informal care, often provided by their partner or family members. The caregiver burden increases with the progression of the patient’s disease. In severe cases, it can lead to psychological distress and burnout, a state of physical and emotional exhaustion [ 14 , 15 , 16 , 17 ].

Mobile health apps, teleconsultation and telemonitoring have been proven to be useful tools in the management and treatment of chronic diseases such as diabetes, heart failure, asthma, chronic obstructive pulmonary disease, and cancer. They have the potential to increase treatment adherence, support self-management, and promote continuity of care [ 18 , 19 , 20 ]. They have the potential to reduce hospital admissions, decrease mortality rates, and lessen health services usage [ 21 , 22 , 23 , 24 ]. The research focus in telemedicine for NMDs varies between disorders. A recent systematic review by Helleman et al. showed telemedicine for ALS patients to be a useful option for remote monitoring, consultations, and follow-ups [ 25 ]. From a patient’s perspective it can be time- and cost-saving while reducing stress and fatigue. While telemedicine has demonstrated its value in certain NMDs like ALS, its usage in the care of other NMDs have not been as extensively studied or described.

This systematic review aims to identify telemedicine interventions for patients with NMDs and analyse the barriers and facilitators of the implementation process associated with telemedicine for NMD patients. The taxonomy by Bashshur et al. will be used to standardise terminology and make it easier to categorise and study the various telehealth interventions and services [ 26 ]. The term “Telemedicine” will be used as an umbrella term to encompass a broad range of remote healthcare services and technologies. This is done to avoid the potential ambiguities and unclarities that can arise from newer terms like "e-health" or "telehealth". This review will provide an overview of the status quo and will offer recommendations for future innovations.

This systematic review followed the PRISMA [ 27 ] checklist. The study protocol was registered on PROSPERO (ID: CRD42022325481).

Databases and search strategy

For the literature search PubMed, Web of Science, and the Cochrane database CENTRAL were used as sources. If full text could not be found, the authors were contacted. The final search was conducted in May of 2022.

The search strategy consisted of two major themes: Firstly, synonyms for NMDs and secondly, synonyms and subcategories for telemedicine. The full search strings can be found in the supplementary file 1 .

Study selection

The study selection was conducted by two reviewers KS and DS. The following inclusion criteria were applied: Studies from any country with any healthcare and insurance system were eligible to maximise the diversity and inclusivity of the evidence base. No restrictions regarding cultural or socio-economic context were made to be truly explorative. Articles were eligible for inclusion if their study population consisted of patients with one or more types of NMDs. Since a single comprehensive list of all NMDs could not be found, the list of NMDs by the Muscular Dystrophy Association (MDA) was used as a reference [ 28 ]. If a disease could not be found under the listed disorders, the International Classification of Diseases (ICD) was consulted [ 29 ]. No limitations regarding sex, age, race, or nationality were made. All types of telemedicine were eligible for inclusion. The taxonomy by Bashshur et al. was used as a guiding definition [ 26 ]. Bashshur uses telemedicine in his paper as the original term for ICT in healthcare. The domains include the following components:

Telehealth : Health behavior & education; Health & disease epidemiology; Environmental/Industrial health; Health management & policy.

E-health : Electronic health record; Health information; Clinical decision support system; Physician order entry.

M-health : Clinical support; Health worker support; Remote data collection; Helplines.

Interventions could be implemented on a national, communal, or institutional level. The users could include patients, caregivers, and healthcare workers. Only primary research was included. Due to the explorative nature of the systematic review, no major restrictions regarding study types were made. Only articles written in English or German were included. Due to the rapid pace of technological progress, only studies from the last ten years were considered. This ensured that the telemedicine interventions were not out-of-date or obsolete.

Studies were excluded if no specific diagnostic group was mentioned. Further reviews, study protocols and commentaries were excluded.

Data extraction and analysis

The data extraction and analysis were done by DS. From the included studies the following data points were extracted: authors, year of publication, country, included NMDs, intervention type and analysed outcomes. Additionally, barriers and facilitators of the implementation process were collected. The Consolidated Framework for Implementation Research (CFIR) was used to guide the extraction process [ 30 ]. The CFIR is an established framework for the analysis of implementation processes. Based on this structure, a detailed coding manual with operationalised definitions for each construct was created. This manual served as a reference guide to ensure that the extraction and coding process was systematic and reproducible.

The data synthesis was done narratively. Since no effect measures were used, a quantitative analysis was not applicable. Firstly, the types of telemedicine interventions were clustered according to the domains described by Bashshur et al., to gain a comprehensive understanding of the current landscape of telemedicine applications [ 26 ]. Secondly, the CFIR was used to label quotes on implementation barriers and facilitators [ 30 ].

No meta-analysis was conducted as there are no quantitative outcomes to analyse. Further, the heterogeneity of the studies was not assessed. Due to the broad inclusion criteria, a high heterogeneity could be expected. Since the focus of this systematic review lies on the intervention types, rather than on their effectiveness, subgroup analyses were not performed. Equally no sensitivity analyses were conducted. The focus of the systematic review was not to summarise evidence regarding a specific intervention, it was an exploration of the current telemedicine options for patients with NMDs.

Risk of bias

The study protocol stated a risk of bias assessment using the RoB 2 and ROBINS-I tools [ 31 , 32 ]. This was later changed to the JBI’s critical appraisal tools as they offered a wider selection of checklists [ 33 ]. No meta-bias was analyzed since the outcomes of the studies were not a point of interest.

Included studies

Figure  1 depicts the study selection process for the systematic review, including a total of 57 reports. These included four report pairs with interlinked content. Ando et al. published two papers on the Intervention Careportal in 2019 and 2021 [ 34 , 35 ]. Hobson et al. conducted one study with results disseminated across two publications [ 36 , 37 ]. Martinet et al. conducted two studies utilising the same intervention but with distinct comparison groups and study populations [ 38 , 39 ]. Lastly, Sobierajska-Rek et al. and Wasilewska et al. published two articles addressing different subsections of one main study [ 40 , 41 ]. Studies excluded during the full text screening process can be found in supplementary file 2 .

Flow diagram of the identified studies (Source: own depiction)

Study characteristics

Table 1 presents an overview of the study characteristics. A total of 25 studies were carried out using a cross-sectional design [ 34 , 35 , 40 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ]. Additionally, the review included two case series [ 64 , 65 ] and one case–control study [ 66 ]. Among the studies, 16 adopted a cohort study design [ 41 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 ], while ten employed an experimental design [ 36 , 37 , 38 , 39 , 82 , 83 , 84 , 85 , 86 , 87 ]. The remaining three reports were method papers [ 88 , 89 , 90 ]. Geographically, the majority of the studies took place in Europe [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 47 , 48 , 49 , 53 , 58 , 59 , 60 , 62 , 65 , 66 , 68 , 69 , 72 , 73 , 74 , 76 , 80 , 81 , 82 , 84 , 87 , 88 , 89 , 90 ] and the USA [ 44 , 45 , 46 , 51 , 52 , 54 , 56 , 57 , 63 , 67 , 71 , 77 , 78 , 79 , 85 , 86 ]. Two studies were conducted in Canada [ 50 , 64 ] and one in each of the following countries: Japan [ 70 ], India [ 75 ], Brazil [ 83 ], and Australia [ 55 ]. One study included participants from around the globe [ 61 ].

A total of twenty-nine studies exclusively focussed on ALS patients [ 34 , 35 , 36 , 37 , 42 , 46 , 48 , 51 , 52 , 55 , 56 , 57 , 62 , 69 , 70 , 71 , 72 , 73 , 76 , 77 , 78 , 79 , 80 , 81 , 85 , 86 , 87 , 88 , 89 , 90 ], while another four studies included ALS patients alongside other NMD diagnostic groups [ 53 , 54 , 58 , 60 ]. The study outcomes assessed in these studies varied widely. Clinical outcomes, such as physical and cognitive function, as well as mental health, were often used. Further, user satisfaction and utilisation measurements were applied to evaluate interventions. For patient registry studies, epidemiological statistics, including prevalence, were commonly employed as outcome measures.

While the primary focus has been on exploring the availability of telemedicine interventions for patients with NMDS, it is crucial not to overlook the evaluation of individual study quality and the potential impact of bias. In summary, most studies demonstrated a low risk of bias and employed sound methods and procedures. However, certain limitations, such as the lack of comparison groups, insufficient follow up time, and some inadequate reporting, should be noted. Visual depictions and the complete analysis can be found in supplementary material 3 . Three reports were not assessed as they only presented a method paper without empirical results [ 88 , 89 , 90 ].

Telemedicine domains of included interventions

In the following sections the telemedicine interventions included in the analysis will be examined, guided by the taxonomy by Bashshur et al [ 26 ]. According to their definition, telemedicine comprises of three major domains: telehealth, e-health, and m-health . Eight studies were categorised under the telehealth domain, encompassing all traditional public health areas. E-health, mainly describing the online storage of information and supporting tools for physicians, was represented by ten studies. The majority of studies fell within the m-health domain, a rapidly growing field that leverages mobile devices like smartphones and tablets to deliver healthcare services, monitor patients remotely, and support self-management. Given that interventions could encompass elements from different domains, multiple mentions or references to different domains is possible. As stated, there were instances where multiple reports featured identical telemedicine interventions [ 34 , 35 , 36 , 37 , 38 , 39 ]. In order not to bias the results, identical interventions were counted as one during the analysis of the telemedicine domains and components. The distribution of telemedicine domains is illustrated in Fig.  2 a.

Distribution of the telemedicine ( a ) and telehealth domains ( b ) (Source: own depiction)

The studies within the telehealth domain were mostly epidemiological studies. Six studies described online patient registries for one or more NMDs [ 43 , 44 , 47 , 56 , 58 , 72 ]. The remaining two studies were categorised under health education. One study introduced a blended curriculum focusing on physical examinations for patients with NMDs [ 45 ] while another detailed a virtual neuromuscular ultrasound course [ 61 ]. The distribution of the telehealth domain can be seen in Fig.  2 b.

The second smallest domain was e-health (Fig.  3 ). Within this domain, three studies incorporated electronic health records [ 69 , 89 , 90 ]. Health information was the subject of five studies, with two of these not providing an intervention but instead investigating patients’ computer use and information seeking behaviour [ 42 , 50 ]. Only two interventions described clinical decision support systems, one supporting physicians during the diagnostic phase [ 53 ] and another supporting patients with advanced care planning [ 71 ]. A singular app used a function for physician order entries, specifically for nutrition plan entries [ 86 ].

Distribution of the e-Health ( a ) and m-health domains ( b ) (Source: own depiction)

Most included studies contained m-health components (Fig.  3 ). Among the various m-health interventions analysed, helplines represented the smallest category. Specifically, four interventions provided emergency telephone support, and one included useful helpline numbers in their app [ 35 , 60 , 81 , 89 , 90 ].

The predominant categories within the m-health domain were clinical support and remote data collection. Nine studies reported interventions with synchronous consultations and data collection [ 40 , 51 , 57 , 62 , 65 , 73 , 75 , 81 , 85 ]. To illustrate, Christodoulou et al. conducted telephone-based cognitive-behavioural screening in ALS patients [ 85 ], demonstrating how telemedicine can seamlessly combine remote data collection processes with distance consultations. Another example was the remote application of the ALS Functioning Rating Scale during teleconsultations [ 62 ]. An alternative approach identified involving clinical support and remote data collection occurring asynchronously, utilising specially designed devices or mobile applications for data collection [ 35 , 66 , 68 , 69 , 70 , 88 , 90 ]. In this approach, clinical consultation was offered either on demand or automatically triggered based on the collected data.

Fourteen studies used clinical support without remote data collection, including home exercise programs [ 40 , 82 , 83 , 84 ], psychological interventions [ 39 , 87 ] and pure teleconsultation [ 52 , 54 , 55 , 60 , 77 , 78 , 79 ]. In contrast, 12 studies focussed on pure remote data collection without clinical support. This included, accelerometers [ 74 , 80 ], physical assessments [ 63 , 64 , 67 , 76 ] or the assessment of the patient’s nutritional status [ 86 ] or disease-related health [ 37 , 46 , 89 ]. Additionally, Cesareo et al. as well as Wasilewska et al. examined remote pulmonary monitoring devices [ 41 , 49 ].

Barriers and facilitators for the implementation of telemedicine

CFIR was used to assess factors that may facilitate or hinder the implementation of telemedicine. This framework consists of five domains: the inner setting, the outer setting, the implementation process, the intervention characteristics, and the characteristics of the individuals. Relevant information was found in 22 studies, with a predominant focus on patient and carer perspectives [ 34 , 36 , 37 , 41 , 42 , 48 , 49 , 50 , 51 , 52 , 55 , 62 , 63 , 66 , 69 , 73 , 74 , 75 , 76 , 77 , 78 , 83 ]. As a result, no information regarding the inner/outer setting or the implementation process was gathered. All statements focused on the intervention characteristics or the characteristics of the individuals. Thus, the following section is structured according to the two domains and their constructs.

Intervention characteristics

A summary of mentioned barriers and facilitators can be seen in Table  2 .

General characteristics

This category summarises all barriers and facilitators directly linked to the intervention that could not be categorised elsewhere. The most common barrier encountered during the implementation of telemedicine interventions were malfunctions related to internet connectivity or end devices. Examples included software errors [ 51 ], faulty data transmission [ 34 ] or a poor internet connection [ 83 ]. Additionally, it was reported, that the internet and necessary end devices, such as smartphones, tablets, or computers, were often not available [ 48 , 50 , 63 ].

Relative advantage

A major factor for patients was the reduced time and travel burden [ 34 , 51 , 52 , 62 , 76 , 78 ]. In more advanced stages of the diseases travelling with medical equipment became almost impossible, making telemedicine vital for house-bound patients [ 78 ].

Telemonitoring and the remote data collection provided multiple advantages, with patients and caregivers highlighting the timeliness of actions in case of alerts [ 34 , 73 ]. Continuous monitoring also proved beneficial for in-person visits, as medical staff stated that appointments could be used more efficiently with data being analysed beforehand [ 69 ]. Some disadvantages regarding telemedicine were acknowledged. Caregivers and physicians noted the lack of physical evaluation as problematic [ 51 , 52 ]. Additionally, an emotional distance and a lack of informal encounters between patients and healthcare workers was reported [ 52 , 55 ].

Adaptability

Patients appreciated the flexibility of online exercise programs, which were easier to integrate into their daily routines [ 83 ]. It was seen as important to be able to choose the main form of communication [ 55 , 62 ]. For example, patients with speech difficulties communicating via E-Mail was preferred.

Interventions were easier implemented if participants were thoroughly informed about the telemedicine service and if a computer-literate person was on-site [ 78 ]. The duration and frequency of sessions was another major point. Overall, more frequent, and shorter sessions were perceived as less fatiguing [ 78 ].

Design and quality

Critical considerations included the presentation, design, and quality of telemedicine products, emphasizing features like accessible closing mechanisms for wearable devices and age-appropriate designs [ 49 , 74 ].

From a patient’s perspective telemedicine was cost-saving due to reduced travel [ 34 , 48 ]. Nevertheless, acquisition costs could be a barrier for some. Institutional perspectives indicated potential savings, ranging from 20 to 89%, depending on the approach, making costs a crucial factor [ 50 , 77 ].

Characteristics of individuals

The second domain related to the characteristics of individuals. This includes all stakeholders such as patients, caregivers, and healthcare workers. Table 3 depicts the barriers and facilitators relating to the characteristics of individuals.

Knowledge and Beliefs about the Intervention

The CFIR highlights the importance of an individual’s pre-existing knowledge and beliefs about the intervention [ 30 ]. Trust in the intervention was vital for patients using telemonitoring [ 34 , 36 , 52 , 69 , 78 ]. This includes being confident that the transmitted data was monitored and that providers would act in the case of abnormalities.

Self-efficacy

Easy to use technology was seen as an enabler for telemedicine implementation, as it reassured the user in their abilities. Accordingly, barriers arose if patients could not or did not feel confident in using technological devices [ 50 , 51 , 69 ]. Lack of confidence led patients to use technology on rare occasions and only if deemed necessary [ 36 ].

Other personal attributes

Lastly, this category summarises all personal traits of stakeholders that might impact the implementation of the intervention [ 30 ]. Younger, higher-educated patients embraced technology more readily [ 42 , 75 ]. Another enabler was telemonitoring improving patient empowerment, symptom awareness, and communication [ 34 , 36 , 51 , 69 ]. However, some found constant disease confrontation challenging [ 69 ]. Lastly, a personal connection with medical staff enabled telemedicine use [ 36 ].

This systematic review presents a comprehensive overview of the current status of telemedicine applications for patients with NMDs. The primary objective was to classify the identified interventions according to the dimensions of telemedicine. While some studies within this review explored the epidemiology of NMDs, and two interventions provided education for clinical staff, it's clear that certain aspects of telemedicine in public health remain under-studied.

E-health, encompassing health information, an electronic health record or physician order entries/treatment instructions, was comparatively underutilised, with only a subset of interventions included. Moreover, decision support systems were rarely investigated. The predominant focus of most interventions was on clinical support and remote data collection.

The second phase of the analysis concentrated on the implementation process, with a specific focus on identifying barriers and facilitators associated with both the intervention itself and the individuals involved. In comparison to traditional care, telemedicine often demonstrated a relative advantage. The high motivation demonstrated by NMD patients and their caregivers in integrating telemedicine into their care plan is a testament to the potential of telemedicine as a transformative force in healthcare.

Telemedicine was often perceived as a resource-saving, less fatiguing alternative, particularly offering increased accessibility for homebound patients. The lack of physical touch and reduced personal connections emerged as significant barriers. Additionally, the accessibility of technology played a pivotal role, as inadequate design hindered some patients from using telemedicine services. The acceptance and uptake of telemedicine services often depended on the readiness of patients and their caregivers to embrace and adapt to new digital solutions. Recognising the importance of patient empowerment, fostering the development of essential skills and confidence in utilising technology is crucial for enabling patients to actively engage in their healthcare.

Clinical and policy implications

The COVID-19 pandemic created an unprecedented opportunity for the development and implementation of telehealth. Disruptions in healthcare access, caused by social distancing and hygiene guidelines, led healthcare practitioners to expand telemedicine services to ensure the continuity of care [ 91 , 92 ]. This trend extended to the field of neuromuscular disease care as well [ 48 , 62 , 91 , 93 , 94 ]. The American Academy of Neurology's "Telehealth Position Statement" endorsed telemedicine, citing benefits such as improved access, reduced costs, and enhanced comfort, aligning with findings in this review [ 95 ].

Our findings further highlighted important considerations for the successful implementation of telemedicine. Firstly, it is essential to recognise that not all geographic locations are equally suited for telehealth. Remote areas with insufficient internet or cell phone coverage, as well as low-income households with a lack of digital technologies, may encounter difficulties in participating in telemedicine interventions [ 96 ]. Secondly, careful selection of the target population is vital, as the attitude and willingness of users significantly impact technology uptake [ 34 , 36 , 52 , 69 , 78 ]. The acceptance and efficacy of telemedicine interventions are inherently intertwined with diverse cultural attitudes towards healthcare and technology.

Therefore, understanding cultural factors is critical to discern how these variables may influence the successful integration of telehealth programs across diverse patient populations. A systematic analysis of cultural competence would provide valuable insights to refine and customise approaches, meeting the distinctive needs of diverse communities. Such considerations not only enhance the inclusivity of telemedicine but also contribute to its overall effectiveness and acceptance among a broad spectrum of individuals.

As the results have shown, it is vital to adapt telemedicine to the specific and evolving needs of patients with NMDs. These needs not only vary from patient to patient but also change over time as the disease progresses [ 5 ]. Therefore, when designing telemedicine technology for patients with NMDs, emphasis should be placed on adaptability, flexibility and accessibility [ 49 , 55 , 62 , 74 , 83 ].

Designing telemedicine technology that caters for the unique challenges faced by patients with physical disabilities and cognitive impairments is crucial for fostering inclusive healthcare [ 49 , 74 ]. User interfaces need to incorporate accessibility features, such as voice commands, large fonts, and intuitive navigation, to accommodate individuals with motor challenges or cognitive limitations. Additionally, instructions and information must be presented in various accessible formats, accommodating diverse learning needs [ 97 ].

Prioritising plain language and ensuring readability at lower literacy levels is essential. This approach not only makes instructions universally accessible but also empowers all patients to effectively participate in telemedicine interactions. By incorporating these considerations into the design, telemedicine can better serve the needs of patients with NMDs, promoting inclusivity and enhancing the overall effectiveness of healthcare delivery [ 97 ].

Health policies and regulatory frameworks play a significant role in influencing the development and adoption of telehealth practices. A nuanced understanding of these regulations, encompassing aspects such as licensure, reimbursement, and liability, is essential for gaining comprehensive insights into the complex landscape that shapes and governs telemedicine [ 96 ]. The intricate web of reimbursement policies directly influences the economic viability of telemedicine services, impacting both healthcare providers and patients. By navigating and understanding these policy and regulatory intricacies, stakeholders in the telemedicine ecosystem can strategically address and potentially overcome barriers, facilitating a more widespread and effective implementation of telehealth services [ 96 ].

This review reveals that telemedicine interventions for patients with NMDs exist but have yet to realise their full protentional. Firstly, the heavy focus on ALS care should be expanded to encompass all diagnostic groups within the NMD spectrum. Especially the high availability of mHealth applications, which could be seamlessly integrated into care plans. This integration has the potential to enhance continuity of care, simultaneously easing the burden on the healthcare system and reducing appointment frequency for patients [ 69 ].

The incorporation of long-term patient data through remote monitoring holds numerous advantages [ 98 , 99 ]. Continuous data collection could offer enhanced insights into disease progression, thereby improving disease management. Given the degenerative nature of most NMDs, there is a speculation that long-term data could help in detecting early signs of deterioration, facilitating quicker adaption of treatments. Furthermore, detailed information about disease progression could contribute to health prognosis, empowering both patients and healthcare professionals to better plan and coordinate care [ 98 , 99 ]. It is evident that the full benefits of telemonitoring remain undiscovered, making it an important and interesting area for future research. The exploration of these untapped potentials could significantly advance the effectiveness and scope of telemedicine in the context of NMDs.

Research and evaluation opportunities

The current telemedicine landscape yields promising results, particularly in its role in supporting rare disease research through the establishment of disease registries. These registries systematically collect patient data related to disease progression and treatment, forming the foundation for observational studies [ 100 , 101 ]. These studies offer critical insights into the management and progression of rare disease, contributing to evidence-based clinical decisions and facilitating the recruitment of participants for clinical trial.

National and international patient registries are pivotal for studying prevalence and incidence, enhancing our understanding of rare diseases like neuromuscular disorders [ 100 , 101 ]. The establishment of global patient registries becomes especially important for pooling data on rare diseases. International collaborations can help bridge the gap in research for understudied NMDs. By fostering collaboration and sharing data on a global scale, telemedicine-supported registries contribute significantly to advancing our understanding and management of rare diseases.

The results of our systematic review highlight a gap in the research on telemedicine for NMDs. Except for ALS, most NMDs are underrepresented in the current body of literature. Future research should include a more diverse range of diagnostic groups and undertake a comparative analysis of challenges and solutions. This would lead to a higher external validity and faster adaption of telemedicine solutions.

While teleconsultation and remote monitoring for NMDs are well described, other critical domains within telemedicine have received comparatively limited attention. These research gaps should be addressed in the future. Most importantly, implementation science has a critical role in the successful deployment of telemedicine interventions for NMDs. As seen in this systematic review studies, the focus needs to be on patients, caregivers, and health care practitioners, as well as the intervention itself.

It is noteworthy that there is underreporting of crucial aspects, such as the inner and outer settings, as well as the implementation process, in telemedicine interventions for NMDs. Additionally, there is need for research examining the impact of health policies and clinical guidelines on the adoption and implementation of telemedicine. The lack of implementation research has been described in the systematic review by Helleman et al., who analysed telemedicine for ALS patients [ 25 ]. Implementation science is needed to improve the efficiency and uptake of future telemedicine interventions for NMDs [ 102 ].

While our systematic review focused on highlighting the barriers and facilitators of telemedicine, we fully recognise the importance of addressing the validation challenges associated with digital health data. Future research and healthcare policies should emphasise the need for robust validation processes to ensure the reliability and clinical relevance of digital outcomes in telemedicine interventions.

Limitations

Despite an extensive search string, additional search terms might have yielded more results, especially considering synonyms for neuromuscular diseases. A more specific search for individual diagnostic groups would have been more inclusive, but the sheer number of NMDs made this unfeasible.

The literature databases used represent common sources of clinical evidence, but they may not comprehensively cover health policies, management, and health education related to NMDs, which might be found in other types of databases.

The absence of experimental study designs in the individual studies was notable, with most included studies being cross-sectional or observational. However, as this review aims to provide an overview of interventions, this description suffices.

The majority of included studies are from high-income countries, and the extent of telemedicine utilisation in low- and middle-income countries remains unclear. The variation in target population size and time horizon in NMD research reflects the complexity and rarity of these conditions, suggesting a need for longer follow-up times in future studies to better describe long-term outcomes.

This systematic review offers a comprehensive view of the telemedicine landscape in the context of NMDs. While domains like teleconsultation and telemonitoring have received extensive attention and reporting in the literature, other critical domains, such as decision support tools and informational support, are notably lacking in research and documentation. To further understand, develop and implement telemedicine solutions and to close existing gaps in NMD-specific healthcare provision, policies and guidelines are needed. By actively integrating telemedicine into existing healthcare plans and maintaining a commitment to ongoing updates and improvements, healthcare systems can optimise care delivery, enhance patient outcomes, and ensure that individuals with NMDs receive the high-quality care they deserve. In addition, more high-quality studies are needed to close research gaps concerning the implementation process of telemedicine and prove the respective efficiency and effectiveness in the long run.

Availability of data and materials

Due to the nature of the paper, no primary data was generated. All data analysed during this study are included in this published article and its supplementary information files.

Abbreviations

Amyotrophic lateral sclerosis

Anti-synthetase syndrome

Becker muscular dystrophy

Consolidated framework for implementation research

Congenital myopathy

Congenital muscular dystrophy

Charcot-Marie-tooth disease

Dermatomyositis

Duchenne muscular dystrophy

Emery-Dreifuss muscular dystrophy

Facioscapulohumeral muscular dystrophy

Hereditary spastic paraparesis

International classification of diseases

Juvenile dermatomyositis

Lambert-Eaton-myasthenic-syndrome

Limb-Girdle muscular dystrophy

Myotonic dystrophy

Muscular dystrophy

Muscular dystrophy association

Myofibrillar myopathies

Myasthenia gravis

Muscle glycogenosis

Necrotizing myositis

Neuromuscular disease

Overlap myositis

Pompe disease

Polymyositis

Post-Polio syndrome

Randomized controlled trial

Spinal and bulbar muscular atrophy

Spinal muscular atrophy

Transthyretin familial amyloid polyneuropathy

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Senyel, D., Senn, K., Boyd, J. et al. A systematic review of telemedicine for neuromuscular diseases: components and determinants of practice. BMC Digit Health 2 , 17 (2024). https://doi.org/10.1186/s44247-024-00078-9

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Cultural Relativity and Acceptance of Embryonic Stem Cell Research

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There is a debate about the ethical implications of using human embryos in stem cell research, which can be influenced by cultural, moral, and social values. This paper argues for an adaptable framework to accommodate diverse cultural and religious perspectives. By using an adaptive ethics model, research protections can reflect various populations and foster growth in stem cell research possibilities.

INTRODUCTION

Stem cell research combines biology, medicine, and technology, promising to alter health care and the understanding of human development. Yet, ethical contention exists because of individuals’ perceptions of using human embryos based on their various cultural, moral, and social values. While these disagreements concerning policy, use, and general acceptance have prompted the development of an international ethics policy, such a uniform approach can overlook the nuanced ethical landscapes between cultures. With diverse viewpoints in public health, a single global policy, especially one reflecting Western ethics or the ethics prevalent in high-income countries, is impractical. This paper argues for a culturally sensitive, adaptable framework for the use of embryonic stem cells. Stem cell policy should accommodate varying ethical viewpoints and promote an effective global dialogue. With an extension of an ethics model that can adapt to various cultures, we recommend localized guidelines that reflect the moral views of the people those guidelines serve.

Stem cells, characterized by their unique ability to differentiate into various cell types, enable the repair or replacement of damaged tissues. Two primary types of stem cells are somatic stem cells (adult stem cells) and embryonic stem cells. Adult stem cells exist in developed tissues and maintain the body’s repair processes. [1] Embryonic stem cells (ESC) are remarkably pluripotent or versatile, making them valuable in research. [2] However, the use of ESCs has sparked ethics debates. Considering the potential of embryonic stem cells, research guidelines are essential. The International Society for Stem Cell Research (ISSCR) provides international stem cell research guidelines. They call for “public conversations touching on the scientific significance as well as the societal and ethical issues raised by ESC research.” [3] The ISSCR also publishes updates about culturing human embryos 14 days post fertilization, suggesting local policies and regulations should continue to evolve as ESC research develops. [4]  Like the ISSCR, which calls for local law and policy to adapt to developing stem cell research given cultural acceptance, this paper highlights the importance of local social factors such as religion and culture.

I.     Global Cultural Perspective of Embryonic Stem Cells

Views on ESCs vary throughout the world. Some countries readily embrace stem cell research and therapies, while others have stricter regulations due to ethical concerns surrounding embryonic stem cells and when an embryo becomes entitled to moral consideration. The philosophical issue of when the “someone” begins to be a human after fertilization, in the morally relevant sense, [5] impacts when an embryo becomes not just worthy of protection but morally entitled to it. The process of creating embryonic stem cell lines involves the destruction of the embryos for research. [6] Consequently, global engagement in ESC research depends on social-cultural acceptability.

a.     US and Rights-Based Cultures

In the United States, attitudes toward stem cell therapies are diverse. The ethics and social approaches, which value individualism, [7] trigger debates regarding the destruction of human embryos, creating a complex regulatory environment. For example, the 1996 Dickey-Wicker Amendment prohibited federal funding for the creation of embryos for research and the destruction of embryos for “more than allowed for research on fetuses in utero.” [8] Following suit, in 2001, the Bush Administration heavily restricted stem cell lines for research. However, the Stem Cell Research Enhancement Act of 2005 was proposed to help develop ESC research but was ultimately vetoed. [9] Under the Obama administration, in 2009, an executive order lifted restrictions allowing for more development in this field. [10] The flux of research capacity and funding parallels the different cultural perceptions of human dignity of the embryo and how it is socially presented within the country’s research culture. [11]

b.     Ubuntu and Collective Cultures

African bioethics differs from Western individualism because of the different traditions and values. African traditions, as described by individuals from South Africa and supported by some studies in other African countries, including Ghana and Kenya, follow the African moral philosophies of Ubuntu or Botho and Ukama , which “advocates for a form of wholeness that comes through one’s relationship and connectedness with other people in the society,” [12] making autonomy a socially collective concept. In this context, for the community to act autonomously, individuals would come together to decide what is best for the collective. Thus, stem cell research would require examining the value of the research to society as a whole and the use of the embryos as a collective societal resource. If society views the source as part of the collective whole, and opposes using stem cells, compromising the cultural values to pursue research may cause social detachment and stunt research growth. [13] Based on local culture and moral philosophy, the permissibility of stem cell research depends on how embryo, stem cell, and cell line therapies relate to the community as a whole. Ubuntu is the expression of humanness, with the person’s identity drawn from the “’I am because we are’” value. [14] The decision in a collectivistic culture becomes one born of cultural context, and individual decisions give deference to others in the society.

Consent differs in cultures where thought and moral philosophy are based on a collective paradigm. So, applying Western bioethical concepts is unrealistic. For one, Africa is a diverse continent with many countries with different belief systems, access to health care, and reliance on traditional or Western medicines. Where traditional medicine is the primary treatment, the “’restrictive focus on biomedically-related bioethics’” [is] problematic in African contexts because it neglects bioethical issues raised by traditional systems.” [15] No single approach applies in all areas or contexts. Rather than evaluating the permissibility of ESC research according to Western concepts such as the four principles approach, different ethics approaches should prevail.

Another consideration is the socio-economic standing of countries. In parts of South Africa, researchers have not focused heavily on contributing to the stem cell discourse, either because it is not considered health care or a health science priority or because resources are unavailable. [16] Each country’s priorities differ given different social, political, and economic factors. In South Africa, for instance, areas such as maternal mortality, non-communicable diseases, telemedicine, and the strength of health systems need improvement and require more focus [17] Stem cell research could benefit the population, but it also could divert resources from basic medical care. Researchers in South Africa adhere to the National Health Act and Medicines Control Act in South Africa and international guidelines; however, the Act is not strictly enforced, and there is no clear legislation for research conduct or ethical guidelines. [18]

Some parts of Africa condemn stem cell research. For example, 98.2 percent of the Tunisian population is Muslim. [19] Tunisia does not permit stem cell research because of moral conflict with a Fatwa. Religion heavily saturates the regulation and direction of research. [20] Stem cell use became permissible for reproductive purposes only recently, with tight restrictions preventing cells from being used in any research other than procedures concerning ART/IVF.  Their use is conditioned on consent, and available only to married couples. [21] The community's receptiveness to stem cell research depends on including communitarian African ethics.

c.     Asia

Some Asian countries also have a collective model of ethics and decision making. [22] In China, the ethics model promotes a sincere respect for life or human dignity, [23] based on protective medicine. This model, influenced by Traditional Chinese Medicine (TCM), [24] recognizes Qi as the vital energy delivered via the meridians of the body; it connects illness to body systems, the body’s entire constitution, and the universe for a holistic bond of nature, health, and quality of life. [25] Following a protective ethics model, and traditional customs of wholeness, investment in stem cell research is heavily desired for its applications in regenerative therapies, disease modeling, and protective medicines. In a survey of medical students and healthcare practitioners, 30.8 percent considered stem cell research morally unacceptable while 63.5 percent accepted medical research using human embryonic stem cells. Of these individuals, 89.9 percent supported increased funding for stem cell research. [26] The scientific community might not reflect the overall population. From 1997 to 2019, China spent a total of $576 million (USD) on stem cell research at 8,050 stem cell programs, increased published presence from 0.6 percent to 14.01 percent of total global stem cell publications as of 2014, and made significant strides in cell-based therapies for various medical conditions. [27] However, while China has made substantial investments in stem cell research and achieved notable progress in clinical applications, concerns linger regarding ethical oversight and transparency. [28] For example, the China Biosecurity Law, promoted by the National Health Commission and China Hospital Association, attempted to mitigate risks by introducing an institutional review board (IRB) in the regulatory bodies. 5800 IRBs registered with the Chinese Clinical Trial Registry since 2021. [29] However, issues still need to be addressed in implementing effective IRB review and approval procedures.

The substantial government funding and focus on scientific advancement have sometimes overshadowed considerations of regional cultures, ethnic minorities, and individual perspectives, particularly evident during the one-child policy era. As government policy adapts to promote public stability, such as the change from the one-child to the two-child policy, [30] research ethics should also adapt to ensure respect for the values of its represented peoples.

Japan is also relatively supportive of stem cell research and therapies. Japan has a more transparent regulatory framework, allowing for faster approval of regenerative medicine products, which has led to several advanced clinical trials and therapies. [31] South Korea is also actively engaged in stem cell research and has a history of breakthroughs in cloning and embryonic stem cells. [32] However, the field is controversial, and there are issues of scientific integrity. For example, the Korean FDA fast-tracked products for approval, [33] and in another instance, the oocyte source was unclear and possibly violated ethical standards. [34] Trust is important in research, as it builds collaborative foundations between colleagues, trial participant comfort, open-mindedness for complicated and sensitive discussions, and supports regulatory procedures for stakeholders. There is a need to respect the culture’s interest, engagement, and for research and clinical trials to be transparent and have ethical oversight to promote global research discourse and trust.

d.     Middle East

Countries in the Middle East have varying degrees of acceptance of or restrictions to policies related to using embryonic stem cells due to cultural and religious influences. Saudi Arabia has made significant contributions to stem cell research, and conducts research based on international guidelines for ethical conduct and under strict adherence to guidelines in accordance with Islamic principles. Specifically, the Saudi government and people require ESC research to adhere to Sharia law. In addition to umbilical and placental stem cells, [35] Saudi Arabia permits the use of embryonic stem cells as long as they come from miscarriages, therapeutic abortions permissible by Sharia law, or are left over from in vitro fertilization and donated to research. [36] Laws and ethical guidelines for stem cell research allow the development of research institutions such as the King Abdullah International Medical Research Center, which has a cord blood bank and a stem cell registry with nearly 10,000 donors. [37] Such volume and acceptance are due to the ethical ‘permissibility’ of the donor sources, which do not conflict with religious pillars. However, some researchers err on the side of caution, choosing not to use embryos or fetal tissue as they feel it is unethical to do so. [38]

Jordan has a positive research ethics culture. [39] However, there is a significant issue of lack of trust in researchers, with 45.23 percent (38.66 percent agreeing and 6.57 percent strongly agreeing) of Jordanians holding a low level of trust in researchers, compared to 81.34 percent of Jordanians agreeing that they feel safe to participate in a research trial. [40] Safety testifies to the feeling of confidence that adequate measures are in place to protect participants from harm, whereas trust in researchers could represent the confidence in researchers to act in the participants’ best interests, adhere to ethical guidelines, provide accurate information, and respect participants’ rights and dignity. One method to improve trust would be to address communication issues relevant to ESC. Legislation surrounding stem cell research has adopted specific language, especially concerning clarification “between ‘stem cells’ and ‘embryonic stem cells’” in translation. [41] Furthermore, legislation “mandates the creation of a national committee… laying out specific regulations for stem-cell banking in accordance with international standards.” [42] This broad regulation opens the door for future global engagement and maintains transparency. However, these regulations may also constrain the influence of research direction, pace, and accessibility of research outcomes.

e.     Europe

In the European Union (EU), ethics is also principle-based, but the principles of autonomy, dignity, integrity, and vulnerability are interconnected. [43] As such, the opportunity for cohesion and concessions between individuals’ thoughts and ideals allows for a more adaptable ethics model due to the flexible principles that relate to the human experience The EU has put forth a framework in its Convention for the Protection of Human Rights and Dignity of the Human Being allowing member states to take different approaches. Each European state applies these principles to its specific conventions, leading to or reflecting different acceptance levels of stem cell research. [44]

For example, in Germany, Lebenzusammenhang , or the coherence of life, references integrity in the unity of human culture. Namely, the personal sphere “should not be subject to external intervention.” [45]  Stem cell interventions could affect this concept of bodily completeness, leading to heavy restrictions. Under the Grundgesetz, human dignity and the right to life with physical integrity are paramount. [46] The Embryo Protection Act of 1991 made producing cell lines illegal. Cell lines can be imported if approved by the Central Ethics Commission for Stem Cell Research only if they were derived before May 2007. [47] Stem cell research respects the integrity of life for the embryo with heavy specifications and intense oversight. This is vastly different in Finland, where the regulatory bodies find research more permissible in IVF excess, but only up to 14 days after fertilization. [48] Spain’s approach differs still, with a comprehensive regulatory framework. [49] Thus, research regulation can be culture-specific due to variations in applied principles. Diverse cultures call for various approaches to ethical permissibility. [50] Only an adaptive-deliberative model can address the cultural constructions of self and achieve positive, culturally sensitive stem cell research practices. [51]

II.     Religious Perspectives on ESC

Embryonic stem cell sources are the main consideration within religious contexts. While individuals may not regard their own religious texts as authoritative or factual, religion can shape their foundations or perspectives.

The Qur'an states:

“And indeed We created man from a quintessence of clay. Then We placed within him a small quantity of nutfa (sperm to fertilize) in a safe place. Then We have fashioned the nutfa into an ‘alaqa (clinging clot or cell cluster), then We developed the ‘alaqa into mudgha (a lump of flesh), and We made mudgha into bones, and clothed the bones with flesh, then We brought it into being as a new creation. So Blessed is Allah, the Best of Creators.” [52]

Many scholars of Islam estimate the time of soul installment, marked by the angel breathing in the soul to bring the individual into creation, as 120 days from conception. [53] Personhood begins at this point, and the value of life would prohibit research or experimentation that could harm the individual. If the fetus is more than 120 days old, the time ensoulment is interpreted to occur according to Islamic law, abortion is no longer permissible. [54] There are a few opposing opinions about early embryos in Islamic traditions. According to some Islamic theologians, there is no ensoulment of the early embryo, which is the source of stem cells for ESC research. [55]

In Buddhism, the stance on stem cell research is not settled. The main tenets, the prohibition against harming or destroying others (ahimsa) and the pursuit of knowledge (prajña) and compassion (karuna), leave Buddhist scholars and communities divided. [56] Some scholars argue stem cell research is in accordance with the Buddhist tenet of seeking knowledge and ending human suffering. Others feel it violates the principle of not harming others. Finding the balance between these two points relies on the karmic burden of Buddhist morality. In trying to prevent ahimsa towards the embryo, Buddhist scholars suggest that to comply with Buddhist tenets, research cannot be done as the embryo has personhood at the moment of conception and would reincarnate immediately, harming the individual's ability to build their karmic burden. [57] On the other hand, the Bodhisattvas, those considered to be on the path to enlightenment or Nirvana, have given organs and flesh to others to help alleviate grieving and to benefit all. [58] Acceptance varies on applied beliefs and interpretations.

Catholicism does not support embryonic stem cell research, as it entails creation or destruction of human embryos. This destruction conflicts with the belief in the sanctity of life. For example, in the Old Testament, Genesis describes humanity as being created in God’s image and multiplying on the Earth, referencing the sacred rights to human conception and the purpose of development and life. In the Ten Commandments, the tenet that one should not kill has numerous interpretations where killing could mean murder or shedding of the sanctity of life, demonstrating the high value of human personhood. In other books, the theological conception of when life begins is interpreted as in utero, [59] highlighting the inviolability of life and its formation in vivo to make a religious point for accepting such research as relatively limited, if at all. [60] The Vatican has released ethical directives to help apply a theological basis to modern-day conflicts. The Magisterium of the Church states that “unless there is a moral certainty of not causing harm,” experimentation on fetuses, fertilized cells, stem cells, or embryos constitutes a crime. [61] Such procedures would not respect the human person who exists at these stages, according to Catholicism. Damages to the embryo are considered gravely immoral and illicit. [62] Although the Catholic Church officially opposes abortion, surveys demonstrate that many Catholic people hold pro-choice views, whether due to the context of conception, stage of pregnancy, threat to the mother’s life, or for other reasons, demonstrating that practicing members can also accept some but not all tenets. [63]

Some major Jewish denominations, such as the Reform, Conservative, and Reconstructionist movements, are open to supporting ESC use or research as long as it is for saving a life. [64] Within Judaism, the Talmud, or study, gives personhood to the child at birth and emphasizes that life does not begin at conception: [65]

“If she is found pregnant, until the fortieth day it is mere fluid,” [66]

Whereas most religions prioritize the status of human embryos, the Halakah (Jewish religious law) states that to save one life, most other religious laws can be ignored because it is in pursuit of preservation. [67] Stem cell research is accepted due to application of these religious laws.

We recognize that all religions contain subsets and sects. The variety of environmental and cultural differences within religious groups requires further analysis to respect the flexibility of religious thoughts and practices. We make no presumptions that all cultures require notions of autonomy or morality as under the common morality theory , which asserts a set of universal moral norms that all individuals share provides moral reasoning and guides ethical decisions. [68] We only wish to show that the interaction with morality varies between cultures and countries.

III.     A Flexible Ethical Approach

The plurality of different moral approaches described above demonstrates that there can be no universally acceptable uniform law for ESC on a global scale. Instead of developing one standard, flexible ethical applications must be continued. We recommend local guidelines that incorporate important cultural and ethical priorities.

While the Declaration of Helsinki is more relevant to people in clinical trials receiving ESC products, in keeping with the tradition of protections for research subjects, consent of the donor is an ethical requirement for ESC donation in many jurisdictions including the US, Canada, and Europe. [69] The Declaration of Helsinki provides a reference point for regulatory standards and could potentially be used as a universal baseline for obtaining consent prior to gamete or embryo donation.

For instance, in Columbia University’s egg donor program for stem cell research, donors followed standard screening protocols and “underwent counseling sessions that included information as to the purpose of oocyte donation for research, what the oocytes would be used for, the risks and benefits of donation, and process of oocyte stimulation” to ensure transparency for consent. [70] The program helped advance stem cell research and provided clear and safe research methods with paid participants. Though paid participation or covering costs of incidental expenses may not be socially acceptable in every culture or context, [71] and creating embryos for ESC research is illegal in many jurisdictions, Columbia’s program was effective because of the clear and honest communications with donors, IRBs, and related stakeholders.  This example demonstrates that cultural acceptance of scientific research and of the idea that an egg or embryo does not have personhood is likely behind societal acceptance of donating eggs for ESC research. As noted, many countries do not permit the creation of embryos for research.

Proper communication and education regarding the process and purpose of stem cell research may bolster comprehension and garner more acceptance. “Given the sensitive subject material, a complete consent process can support voluntary participation through trust, understanding, and ethical norms from the cultures and morals participants value. This can be hard for researchers entering countries of different socioeconomic stability, with different languages and different societal values. [72]

An adequate moral foundation in medical ethics is derived from the cultural and religious basis that informs knowledge and actions. [73] Understanding local cultural and religious values and their impact on research could help researchers develop humility and promote inclusion.

IV.     Concerns

Some may argue that if researchers all adhere to one ethics standard, protection will be satisfied across all borders, and the global public will trust researchers. However, defining what needs to be protected and how to define such research standards is very specific to the people to which standards are applied. We suggest that applying one uniform guide cannot accurately protect each individual because we all possess our own perceptions and interpretations of social values. [74] Therefore, the issue of not adjusting to the moral pluralism between peoples in applying one standard of ethics can be resolved by building out ethics models that can be adapted to different cultures and religions.

Other concerns include medical tourism, which may promote health inequities. [75] Some countries may develop and approve products derived from ESC research before others, compromising research ethics or drug approval processes. There are also concerns about the sale of unauthorized stem cell treatments, for example, those without FDA approval in the United States. Countries with robust research infrastructures may be tempted to attract medical tourists, and some customers will have false hopes based on aggressive publicity of unproven treatments. [76]

For example, in China, stem cell clinics can market to foreign clients who are not protected under the regulatory regimes. Companies employ a marketing strategy of “ethically friendly” therapies. Specifically, in the case of Beike, China’s leading stem cell tourism company and sprouting network, ethical oversight of administrators or health bureaus at one site has “the unintended consequence of shifting questionable activities to another node in Beike's diffuse network.” [77] In contrast, Jordan is aware of stem cell research’s potential abuse and its own status as a “health-care hub.” Jordan’s expanded regulations include preserving the interests of individuals in clinical trials and banning private companies from ESC research to preserve transparency and the integrity of research practices. [78]

The social priorities of the community are also a concern. The ISSCR explicitly states that guidelines “should be periodically revised to accommodate scientific advances, new challenges, and evolving social priorities.” [79] The adaptable ethics model extends this consideration further by addressing whether research is warranted given the varying degrees of socioeconomic conditions, political stability, and healthcare accessibilities and limitations. An ethical approach would require discussion about resource allocation and appropriate distribution of funds. [80]

While some religions emphasize the sanctity of life from conception, which may lead to public opposition to ESC research, others encourage ESC research due to its potential for healing and alleviating human pain. Many countries have special regulations that balance local views on embryonic personhood, the benefits of research as individual or societal goods, and the protection of human research subjects. To foster understanding and constructive dialogue, global policy frameworks should prioritize the protection of universal human rights, transparency, and informed consent. In addition to these foundational global policies, we recommend tailoring local guidelines to reflect the diverse cultural and religious perspectives of the populations they govern. Ethics models should be adapted to local populations to effectively establish research protections, growth, and possibilities of stem cell research.

For example, in countries with strong beliefs in the moral sanctity of embryos or heavy religious restrictions, an adaptive model can allow for discussion instead of immediate rejection. In countries with limited individual rights and voice in science policy, an adaptive model ensures cultural, moral, and religious views are taken into consideration, thereby building social inclusion. While this ethical consideration by the government may not give a complete voice to every individual, it will help balance policies and maintain the diverse perspectives of those it affects. Embracing an adaptive ethics model of ESC research promotes open-minded dialogue and respect for the importance of human belief and tradition. By actively engaging with cultural and religious values, researchers can better handle disagreements and promote ethical research practices that benefit each society.

This brief exploration of the religious and cultural differences that impact ESC research reveals the nuances of relative ethics and highlights a need for local policymakers to apply a more intense adaptive model.

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[5] Concerning the moral philosophies of stem cell research, our paper does not posit a personal moral stance nor delve into the “when” of human life begins. To read further about the philosophical debate, consider the following sources:

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[7] Socially, at its core, the Western approach to ethics is widely principle-based, autonomy being one of the key factors to ensure a fundamental respect for persons within research. For information regarding autonomy in research, see: Department of Health, Education, and Welfare, & National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (1978). The Belmont Report. Ethical principles and guidelines for the protection of human subjects of research.; For a more in-depth review of autonomy within the US, see: Beauchamp, T. L., & Childress, J. F. (1994). Principles of Biomedical Ethics . Oxford University Press.

[8] Sherley v. Sebelius , 644 F.3d 388 (D.C. Cir. 2011), citing 45 C.F.R. 46.204(b) and [42 U.S.C. § 289g(b)]. https://www.cadc.uscourts.gov/internet/opinions.nsf/6c690438a9b43dd685257a64004ebf99/$file/11-5241-1391178.pdf

[9] Stem Cell Research Enhancement Act of 2005, H. R. 810, 109 th Cong. (2001). https://www.govtrack.us/congress/bills/109/hr810/text ; Bush, G. W. (2006, July 19). Message to the House of Representatives . National Archives and Records Administration. https://georgewbush-whitehouse.archives.gov/news/releases/2006/07/20060719-5.html

[10] National Archives and Records Administration. (2009, March 9). Executive order 13505 -- removing barriers to responsible scientific research involving human stem cells . National Archives and Records Administration. https://obamawhitehouse.archives.gov/the-press-office/removing-barriers-responsible-scientific-research-involving-human-stem-cells

[11] Hurlbut, W. B. (2006). Science, Religion, and the Politics of Stem Cells.  Social Research ,  73 (3), 819–834. http://www.jstor.org/stable/40971854

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[13] Source for further reading: Tangwa G. B. (2007). Moral status of embryonic stem cells: perspective of an African villager. Bioethics , 21(8), 449–457. https://doi.org/10.1111/j.1467-8519.2007.00582.x , see also Mnisi, F. M. (2020). An African analysis based on ethics of Ubuntu - are human embryonic stem cell patents morally justifiable? African Insight , 49 (4).

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[17] Department of Health Republic of South Africa. (2021). Health Research Priorities (revised) for South Africa 2021-2024 . National Health Research Strategy. https://www.health.gov.za/wp-content/uploads/2022/05/National-Health-Research-Priorities-2021-2024.pdf

[18] Oosthuizen, H. (2013). Legal and Ethical Issues in Stem Cell Research in South Africa. In: Beran, R. (eds) Legal and Forensic Medicine. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32338-6_80 , see also: Gaobotse G (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[19] United States Bureau of Citizenship and Immigration Services. (1998). Tunisia: Information on the status of Christian conversions in Tunisia . UNHCR Web Archive. https://webarchive.archive.unhcr.org/20230522142618/https://www.refworld.org/docid/3df0be9a2.html

[20] Gaobotse, G. (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[21] Kooli, C. Review of assisted reproduction techniques, laws, and regulations in Muslim countries.  Middle East Fertil Soc J   24 , 8 (2020). https://doi.org/10.1186/s43043-019-0011-0 ; Gaobotse, G. (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[22] Pang M. C. (1999). Protective truthfulness: the Chinese way of safeguarding patients in informed treatment decisions. Journal of medical ethics , 25(3), 247–253. https://doi.org/10.1136/jme.25.3.247

[23] Wang, L., Wang, F., & Zhang, W. (2021). Bioethics in China’s biosecurity law: Forms, effects, and unsettled issues. Journal of law and the biosciences , 8(1).  https://doi.org/10.1093/jlb/lsab019 https://academic.oup.com/jlb/article/8/1/lsab019/6299199

[24] Wang, Y., Xue, Y., & Guo, H. D. (2022). Intervention effects of traditional Chinese medicine on stem cell therapy of myocardial infarction.  Frontiers in pharmacology ,  13 , 1013740. https://doi.org/10.3389/fphar.2022.1013740

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[30] Chen, H., Wei, T., Wang, H.  et al.  Association of China’s two-child policy with changes in number of births and birth defects rate, 2008–2017.  BMC Public Health   22 , 434 (2022). https://doi.org/10.1186/s12889-022-12839-0

[31] Azuma, K. Regulatory Landscape of Regenerative Medicine in Japan.  Curr Stem Cell Rep   1 , 118–128 (2015). https://doi.org/10.1007/s40778-015-0012-6

[32] Harris, R. (2005, May 19). Researchers Report Advance in Stem Cell Production . NPR. https://www.npr.org/2005/05/19/4658967/researchers-report-advance-in-stem-cell-production

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[34] Resnik, D. B., Shamoo, A. E., & Krimsky, S. (2006). Fraudulent human embryonic stem cell research in South Korea: lessons learned.  Accountability in research ,  13 (1), 101–109. https://doi.org/10.1080/08989620600634193 .

[35] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

[36] Association for the Advancement of Blood and Biotherapies.  https://www.aabb.org/regulatory-and-advocacy/regulatory-affairs/regulatory-for-cellular-therapies/international-competent-authorities/saudi-arabia

[37] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia.  BMC medical ethics ,  21 (1), 35. https://doi.org/10.1186/s12910-020-00482-6

[38] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia. BMC medical ethics , 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

Culturally, autonomy practices follow a relational autonomy approach based on a paternalistic deontological health care model. The adherence to strict international research policies and religious pillars within the regulatory environment is a great foundation for research ethics. However, there is a need to develop locally targeted ethics approaches for research (as called for in Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6), this decision-making approach may help advise a research decision model. For more on the clinical cultural autonomy approaches, see: Alabdullah, Y. Y., Alzaid, E., Alsaad, S., Alamri, T., Alolayan, S. W., Bah, S., & Aljoudi, A. S. (2022). Autonomy and paternalism in Shared decision‐making in a Saudi Arabian tertiary hospital: A cross‐sectional study. Developing World Bioethics , 23 (3), 260–268. https://doi.org/10.1111/dewb.12355 ; Bukhari, A. A. (2017). Universal Principles of Bioethics and Patient Rights in Saudi Arabia (Doctoral dissertation, Duquesne University). https://dsc.duq.edu/etd/124; Ladha, S., Nakshawani, S. A., Alzaidy, A., & Tarab, B. (2023, October 26). Islam and Bioethics: What We All Need to Know . Columbia University School of Professional Studies. https://sps.columbia.edu/events/islam-and-bioethics-what-we-all-need-know

[39] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics.  Research Ethics ,  17 (2), 228-241.  https://doi.org/10.1177/1747016120966779

[40] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics.  Research Ethics ,  17 (2), 228-241.  https://doi.org/10.1177/1747016120966779

[41] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[42] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[43] The EU’s definition of autonomy relates to the capacity for creating ideas, moral insight, decisions, and actions without constraint, personal responsibility, and informed consent. However, the EU views autonomy as not completely able to protect individuals and depends on other principles, such as dignity, which “expresses the intrinsic worth and fundamental equality of all human beings.” Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[44] Council of Europe. Convention for the protection of Human Rights and Dignity of the Human Being with regard to the Application of Biology and Medicine: Convention on Human Rights and Biomedicine (ETS No. 164) https://www.coe.int/en/web/conventions/full-list?module=treaty-detail&treatynum=164 (forbidding the creation of embryos for research purposes only, and suggests embryos in vitro have protections.); Also see Drabiak-Syed B. K. (2013). New President, New Human Embryonic Stem Cell Research Policy: Comparative International Perspectives and Embryonic Stem Cell Research Laws in France.  Biotechnology Law Report ,  32 (6), 349–356. https://doi.org/10.1089/blr.2013.9865

[45] Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[46] Tomuschat, C., Currie, D. P., Kommers, D. P., & Kerr, R. (Trans.). (1949, May 23). Basic law for the Federal Republic of Germany. https://www.btg-bestellservice.de/pdf/80201000.pdf

[47] Regulation of Stem Cell Research in Germany . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-germany

[48] Regulation of Stem Cell Research in Finland . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-finland

[49] Regulation of Stem Cell Research in Spain . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-spain

[50] Some sources to consider regarding ethics models or regulatory oversights of other cultures not covered:

Kara MA. Applicability of the principle of respect for autonomy: the perspective of Turkey. J Med Ethics. 2007 Nov;33(11):627-30. doi: 10.1136/jme.2006.017400. PMID: 17971462; PMCID: PMC2598110.

Ugarte, O. N., & Acioly, M. A. (2014). The principle of autonomy in Brazil: one needs to discuss it ...  Revista do Colegio Brasileiro de Cirurgioes ,  41 (5), 374–377. https://doi.org/10.1590/0100-69912014005013

Bharadwaj, A., & Glasner, P. E. (2012). Local cells, global science: The rise of embryonic stem cell research in India . Routledge.

For further research on specific European countries regarding ethical and regulatory framework, we recommend this database: Regulation of Stem Cell Research in Europe . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-europe   

[51] Klitzman, R. (2006). Complications of culture in obtaining informed consent. The American Journal of Bioethics, 6(1), 20–21. https://doi.org/10.1080/15265160500394671 see also: Ekmekci, P. E., & Arda, B. (2017). Interculturalism and Informed Consent: Respecting Cultural Differences without Breaching Human Rights.  Cultura (Iasi, Romania) ,  14 (2), 159–172.; For why trust is important in research, see also: Gray, B., Hilder, J., Macdonald, L., Tester, R., Dowell, A., & Stubbe, M. (2017). Are research ethics guidelines culturally competent?  Research Ethics ,  13 (1), 23-41.  https://doi.org/10.1177/1747016116650235

[52] The Qur'an  (M. Khattab, Trans.). (1965). Al-Mu’minun, 23: 12-14. https://quran.com/23

[53] Lenfest, Y. (2017, December 8). Islam and the beginning of human life . Bill of Health. https://blog.petrieflom.law.harvard.edu/2017/12/08/islam-and-the-beginning-of-human-life/

[54] Aksoy, S. (2005). Making regulations and drawing up legislation in Islamic countries under conditions of uncertainty, with special reference to embryonic stem cell research. Journal of Medical Ethics , 31: 399-403.; see also: Mahmoud, Azza. "Islamic Bioethics: National Regulations and Guidelines of Human Stem Cell Research in the Muslim World." Master's thesis, Chapman University, 2022. https://doi.org/10.36837/ chapman.000386

[55] Rashid, R. (2022). When does Ensoulment occur in the Human Foetus. Journal of the British Islamic Medical Association , 12 (4). ISSN 2634 8071. https://www.jbima.com/wp-content/uploads/2023/01/2-Ethics-3_-Ensoulment_Rafaqat.pdf.

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[57] Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

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[59] There is no explicit religious reference to when life begins or how to conduct research that interacts with the concept of life. However, these are relevant verses pertaining to how the fetus is viewed. (( King James Bible . (1999). Oxford University Press. (original work published 1769))

Jerimiah 1: 5 “Before I formed thee in the belly I knew thee; and before thou camest forth out of the womb I sanctified thee…”

In prophet Jerimiah’s insight, God set him apart as a person known before childbirth, a theme carried within the Psalm of David.

Psalm 139: 13-14 “…Thou hast covered me in my mother's womb. I will praise thee; for I am fearfully and wonderfully made…”

These verses demonstrate David’s respect for God as an entity that would know of all man’s thoughts and doings even before birth.

[60] It should be noted that abortion is not supported as well.

[61] The Vatican. (1987, February 22). Instruction on Respect for Human Life in Its Origin and on the Dignity of Procreation Replies to Certain Questions of the Day . Congregation For the Doctrine of the Faith. https://www.vatican.va/roman_curia/congregations/cfaith/documents/rc_con_cfaith_doc_19870222_respect-for-human-life_en.html

[62] The Vatican. (2000, August 25). Declaration On the Production and the Scientific and Therapeutic Use of Human Embryonic Stem Cells . Pontifical Academy for Life. https://www.vatican.va/roman_curia/pontifical_academies/acdlife/documents/rc_pa_acdlife_doc_20000824_cellule-staminali_en.html ; Ohara, N. (2003). Ethical Consideration of Experimentation Using Living Human Embryos: The Catholic Church’s Position on Human Embryonic Stem Cell Research and Human Cloning. Department of Obstetrics and Gynecology . Retrieved from https://article.imrpress.com/journal/CEOG/30/2-3/pii/2003018/77-81.pdf.

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[64] Rosner, F., & Reichman, E. (2002). Embryonic stem cell research in Jewish law. Journal of halacha and contemporary society , (43), 49–68.; Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

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[69] World Medical Association (2013). World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA , 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053 Declaration of Helsinki – WMA – The World Medical Association .; see also: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979).  The Belmont report: Ethical principles and guidelines for the protection of human subjects of research . U.S. Department of Health and Human Services.  https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html

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Olivia Bowers

MS Bioethics Columbia University (Disclosure: affiliated with Voices in Bioethics)

Mifrah Hayath

SM Candidate Harvard Medical School, MS Biotechnology Johns Hopkins University

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• RCS England Council discussion on Surgical Care Practitioner scope of practice

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