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Organizing Your Social Sciences Research Paper

  • 8. The Discussion
  • Purpose of Guide
  • Design Flaws to Avoid
  • Independent and Dependent Variables
  • Glossary of Research Terms
  • Reading Research Effectively
  • Narrowing a Topic Idea
  • Broadening a Topic Idea
  • Extending the Timeliness of a Topic Idea
  • Academic Writing Style
  • Applying Critical Thinking
  • Choosing a Title
  • Making an Outline
  • Paragraph Development
  • Research Process Video Series
  • Executive Summary
  • The C.A.R.S. Model
  • Background Information
  • The Research Problem/Question
  • Theoretical Framework
  • Citation Tracking
  • Content Alert Services
  • Evaluating Sources
  • Primary Sources
  • Secondary Sources
  • Tiertiary Sources
  • Scholarly vs. Popular Publications
  • Qualitative Methods
  • Quantitative Methods
  • Insiderness
  • Using Non-Textual Elements
  • Limitations of the Study
  • Common Grammar Mistakes
  • Writing Concisely
  • Avoiding Plagiarism
  • Footnotes or Endnotes?
  • Further Readings
  • Generative AI and Writing
  • USC Libraries Tutorials and Other Guides
  • Bibliography

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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  • How to Write Discussions and Conclusions

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. 

discussion of research findings and application

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:

discussion of research findings and application

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! 

What to do

  • 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. 

What not to do

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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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

researchers writing the discussion section of their research paper

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.

Adam Goulston, Science Marketing Consultant, PsyD, Human and Organizational Behavior, Scize

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How To Write The Discussion Chapter

The what, why & how explained simply (with examples).

By: Jenna Crossley (PhD Cand). 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: Dissertation Discussion Chapter

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

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.

Discussion

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?

discussion of research findings and application

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 .

It depends

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.

Every claim you make must be 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.

discussion of research findings and application

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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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How to write the conclusion chapter of a dissertation

36 Comments

Abbie

Thank you this is helpful!

Sai AKO

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

Nts'eoane Sepanya-Molefi

This has been very helpful indeed. Thank you.

Cheryl

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

Solomon

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

Wongibe Dieudonne

This is really good and explicit. Thanks

Robin MooreZaid

Thank you, this blog has been such a help.

John Amaka

Thank you. This is very helpful.

Syed Firoz Ahmad

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

Kwasi Tonge

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.

Albert Mitugo

Thanks! This is helpful!

Abduljabbar Alsoudani

thanks alot for this informative website

Sudesh Chinthaka

Dear Sir/Madam,

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

Thank you very much!!!

Nann Yin Yin Moe

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.

Eunice Mulenga

This was helpful

Leo Simango

Thanks Jenna, well explained.

Poornima

Thank you! This is super helpful.

William M. Kapambwe

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.

Ahmed

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

Lisha

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!!!!!!!!!

Colbey mwenda

Thanks alot.., I have gained much

Obinna NJOKU

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

Mary Kulabako

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.

vera

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

ahmad yassine

this was a very helpful and useful information

Md Moniruzzaman

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

Salma

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

Mohammed Kwarah Tal

Superlative! More grease to your elbows.

Majani

Powerful, thank you for sharing.

Uno

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!

Joseph Nkitseng

Simplified explanation. Well done.

LE Sibeko

The presentation is enlightening. Thank you very much.

Angela

Thanks for the support and guidance

Beena

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

Yiting W.

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?

Derek Jansen

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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Sacred Heart University Library

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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  • Last Updated: Jul 18, 2023 11:58 AM
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Dissertations 5: findings, analysis and discussion: home.

  • Results/Findings

Alternative Structures

The time has come to show and discuss the findings of your research. How to structure this part of your dissertation? 

Dissertations can have different structures, as you can see in the dissertation  structure  guide.

Dissertations organised by sections

Many dissertations are organised by sections. In this case, we suggest three options. Note that, if within your course you have been instructed to use a specific structure, you should do that. Also note that sometimes there is considerable freedom on the structure, so you can come up with other structures too. 

A) More common for scientific dissertations and quantitative methods:

- Results chapter 

- Discussion chapter

Example: 

  • Introduction
  • Literature review
  • Methodology
  • (Recommendations)

if you write a scientific dissertation, or anyway using quantitative methods, you will have some  objective  results that you will present in the Results chapter. You will then interpret the results in the Discussion chapter.  

B) More common for qualitative methods

- Analysis chapter. This can have more descriptive/thematic subheadings.

- Discussion chapter. This can have more descriptive/thematic subheadings.

  • Case study of Company X (fashion brand) environmental strategies 
  • Successful elements
  • Lessons learnt
  • Criticisms of Company X environmental strategies 
  • Possible alternatives

C) More common for qualitative methods

- Analysis and discussion chapter. This can have more descriptive/thematic titles.

  • Case study of Company X (fashion brand) environmental strategies 

If your dissertation uses qualitative methods, it is harder to identify and report objective data. Instead, it may be more productive and meaningful to present the findings in the same sections where you also analyse, and possibly discuss, them. You will probably have different sections dealing with different themes. The different themes can be subheadings of the Analysis and Discussion (together or separate) chapter(s). 

Thematic dissertations

If the structure of your dissertation is thematic ,  you will have several chapters analysing and discussing the issues raised by your research. The chapters will have descriptive/thematic titles. 

  • Background on the conflict in Yemen (2004-present day)
  • Classification of the conflict in international law  
  • International law violations
  • Options for enforcement of international law
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  • Last Updated: Aug 4, 2023 2:17 PM
  • URL: https://libguides.westminster.ac.uk/c.php?g=696975

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  • Writing Tips

How to Write an “Implications of Research” Section

How to Write an “Implications of Research” Section

4-minute read

  • 24th October 2022

When writing research papers , theses, journal articles, or dissertations, one cannot ignore the importance of research. You’re not only the writer of your paper but also the researcher ! Moreover, it’s not just about researching your topic, filling your paper with abundant citations, and topping it off with a reference list. You need to dig deep into your research and provide related literature on your topic. You must also discuss the implications of your research.

Interested in learning more about implications of research? Read on! This post will define these implications, why they’re essential, and most importantly, how to write them. If you’re a visual learner, you might enjoy this video .

What Are Implications of Research?

Implications are potential questions from your research that justify further exploration. They state how your research findings could affect policies, theories, and/or practices.

Implications can either be practical or theoretical. The former is the direct impact of your findings on related practices, whereas the latter is the impact on the theories you have chosen in your study.

Example of a practical implication: If you’re researching a teaching method, the implication would be how teachers can use that method based on your findings.

Example of a theoretical implication: You added a new variable to Theory A so that it could cover a broader perspective.

Finally, implications aren’t the same as recommendations, and it’s important to know the difference between them .

Questions you should consider when developing the implications section:

●  What is the significance of your findings?

●  How do the findings of your study fit with or contradict existing research on this topic?

●  Do your results support or challenge existing theories? If they support them, what new information do they contribute? If they challenge them, why do you think that is?

Why Are Implications Important?

You need implications for the following reasons:

● To reflect on what you set out to accomplish in the first place

● To see if there’s a change to the initial perspective, now that you’ve collected the data

● To inform your audience, who might be curious about the impact of your research

How to Write an Implications Section

Usually, you write your research implications in the discussion section of your paper. This is the section before the conclusion when you discuss all the hard work you did. Additionally, you’ll write the implications section before making recommendations for future research.

Implications should begin with what you discovered in your study, which differs from what previous studies found, and then you can discuss the implications of your findings.

Your implications need to be specific, meaning you should show the exact contributions of your research and why they’re essential. They should also begin with a specific sentence structure.

Examples of starting implication sentences:

●  These results build on existing evidence of…

●  These findings suggest that…

●  These results should be considered when…

●  While previous research has focused on x , these results show that y …

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You should write your implications after you’ve stated the results of your research. In other words, summarize your findings and put them into context.

The result : One study found that young learners enjoy short activities when learning a foreign language.

The implications : This result suggests that foreign language teachers use short activities when teaching young learners, as they positively affect learning.

 Example 2

The result : One study found that people who listen to calming music just before going to bed sleep better than those who watch TV.

The implications : These findings suggest that listening to calming music aids sleep quality, whereas watching TV does not.

To summarize, remember these key pointers:

●  Implications are the impact of your findings on the field of study.

●  They serve as a reflection of the research you’ve conducted.              

●  They show the specific contributions of your findings and why the audience should care.

●  They can be practical or theoretical.

●  They aren’t the same as recommendations.

●  You write them in the discussion section of the paper.

●  State the results first, and then state their implications.

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Home » Implications in Research – Types, Examples and Writing Guide

Implications in Research – Types, Examples and Writing Guide

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Implications in Research

Implications in Research

Implications in research refer to the potential consequences, applications, or outcomes of the findings and conclusions of a research study. These can include both theoretical and practical implications that extend beyond the immediate scope of the study and may impact various stakeholders, such as policymakers, practitioners, researchers , or the general public.

Structure of Implications

The format of implications in research typically follows the structure below:

  • Restate the main findings: Begin by restating the main findings of the study in a brief summary .
  • Link to the research question/hypothesis : Clearly articulate how the findings are related to the research question /hypothesis.
  • Discuss the practical implications: Discuss the practical implications of the findings, including their potential impact on the field or industry.
  • Discuss the theoretical implications : Discuss the theoretical implications of the findings, including their potential impact on existing theories or the development of new ones.
  • Identify limitations: Identify the limitations of the study and how they may affect the generalizability of the findings.
  • Suggest directions for future research: Suggest areas for future research that could build on the current study’s findings and address any limitations.

Types of Implications in Research

Types of Implications in Research are as follows:

Theoretical Implications

These are the implications that a study has for advancing theoretical understanding in a particular field. For example, a study that finds a new relationship between two variables can have implications for the development of theories and models in that field.

Practical Implications

These are the implications that a study has for solving practical problems or improving real-world outcomes. For example, a study that finds a new treatment for a disease can have implications for improving the health of patients.

Methodological Implications

These are the implications that a study has for advancing research methods and techniques. For example, a study that introduces a new method for data analysis can have implications for how future research in that field is conducted.

Ethical Implications

These are the implications that a study has for ethical considerations in research. For example, a study that involves human participants must consider the ethical implications of the research on the participants and take steps to protect their rights and welfare.

Policy Implications

These are the implications that a study has for informing policy decisions. For example, a study that examines the effectiveness of a particular policy can have implications for policymakers who are considering whether to implement or change that policy.

Societal Implications

These are the implications that a study has for society as a whole. For example, a study that examines the impact of a social issue such as poverty or inequality can have implications for how society addresses that issue.

Forms of Implications In Research

Forms of Implications are as follows:

Positive Implications

These refer to the positive outcomes or benefits that may result from a study’s findings. For example, a study that finds a new treatment for a disease can have positive implications for patients, healthcare providers, and the wider society.

Negative Implications

These refer to the negative outcomes or risks that may result from a study’s findings. For example, a study that finds a harmful side effect of a medication can have negative implications for patients, healthcare providers, and the wider society.

Direct Implications

These refer to the immediate consequences of a study’s findings. For example, a study that finds a new method for reducing greenhouse gas emissions can have direct implications for policymakers and businesses.

Indirect Implications

These refer to the broader or long-term consequences of a study’s findings. For example, a study that finds a link between childhood trauma and mental health issues can have indirect implications for social welfare policies, education, and public health.

Importance of Implications in Research

The following are some of the reasons why implications are important in research:

  • To inform policy and practice: Research implications can inform policy and practice decisions by providing evidence-based recommendations for actions that can be taken to address the issues identified in the research. This can lead to more effective policies and practices that are grounded in empirical evidence.
  • To guide future research: Implications can also guide future research by identifying areas that need further investigation, highlighting gaps in current knowledge, and suggesting new directions for research.
  • To increase the impact of research : By communicating the practical and theoretical implications of their research, researchers can increase the impact of their work by demonstrating its relevance and importance to a wider audience.
  • To enhance the credibility of research : Implications can help to enhance the credibility of research by demonstrating that the findings have practical and theoretical significance and are not just abstract or academic exercises.
  • To foster collaboration and engagement : Implications can also foster collaboration and engagement between researchers, practitioners, policymakers, and other stakeholders by providing a common language and understanding of the practical and theoretical implications of the research.

Example of Implications in Research

Here are some examples of implications in research:

  • Medical research: A study on the efficacy of a new drug for a specific disease can have significant implications for medical practitioners, patients, and pharmaceutical companies. If the drug is found to be effective, it can be used to treat patients with the disease, improve their health outcomes, and generate revenue for the pharmaceutical company.
  • Educational research: A study on the impact of technology on student learning can have implications for educators and policymakers. If the study finds that technology improves student learning outcomes, educators can incorporate technology into their teaching methods, and policymakers can allocate more resources to technology in schools.
  • Social work research: A study on the effectiveness of a new intervention program for individuals with mental health issues can have implications for social workers, mental health professionals, and policymakers. If the program is found to be effective, social workers and mental health professionals can incorporate it into their practice, and policymakers can allocate more resources to the program.
  • Environmental research: A study on the impact of climate change on a particular ecosystem can have implications for environmentalists, policymakers, and industries. If the study finds that the ecosystem is at risk, environmentalists can advocate for policy changes to protect the ecosystem, policymakers can allocate resources to mitigate the impact of climate change, and industries can adjust their practices to reduce their carbon footprint.
  • Economic research: A study on the impact of minimum wage on employment can have implications for policymakers and businesses. If the study finds that increasing the minimum wage does not lead to job losses, policymakers can implement policies to increase the minimum wage, and businesses can adjust their payroll practices.

How to Write Implications in Research

Writing implications in research involves discussing the potential outcomes or consequences of your findings and the practical applications of your study’s results. Here are some steps to follow when writing implications in research:

  • Summarize your key findings: Before discussing the implications of your research, briefly summarize your key findings. This will provide context for your implications and help readers understand how your research relates to your conclusions.
  • Identify the implications: Identify the potential implications of your research based on your key findings. Consider how your results might be applied in the real world, what further research might be necessary, and what other areas of study could be impacted by your research.
  • Connect implications to research question: Make sure that your implications are directly related to your research question or hypotheses. This will help to ensure that your implications are relevant and meaningful.
  • Consider limitations : Acknowledge any limitations or weaknesses of your research, and discuss how these might impact the implications of your research. This will help to provide a more balanced view of your findings.
  • Discuss practical applications : Discuss the practical applications of your research and how your findings could be used in real-world situations. This might include recommendations for policy or practice changes, or suggestions for future research.
  • Be clear and concise : When writing implications in research, be clear and concise. Use simple language and avoid jargon or technical terms that might be confusing to readers.
  • Provide a strong conclusion: Provide a strong conclusion that summarizes your key implications and leaves readers with a clear understanding of the significance of your research.

Purpose of Implications in Research

The purposes of implications in research include:

  • Informing practice: The implications of research can provide guidance for practitioners, policymakers, and other stakeholders about how to apply research findings in practical settings.
  • Generating new research questions: Implications can also inspire new research questions that build upon the findings of the original study.
  • Identifying gaps in knowledge: Implications can help to identify areas where more research is needed to fully understand a phenomenon.
  • Promoting scientific literacy: Implications can also help to promote scientific literacy by communicating research findings in accessible and relevant ways.
  • Facilitating decision-making : The implications of research can assist decision-makers in making informed decisions based on scientific evidence.
  • Contributing to theory development : Implications can also contribute to the development of theories by expanding upon or challenging existing theories.

When to Write Implications in Research

Here are some specific situations of when to write implications in research:

  • Research proposal : When writing a research proposal, it is important to include a section on the potential implications of the research. This section should discuss the potential impact of the research on the field and its potential applications.
  • Literature review : The literature review is an important section of the research paper where the researcher summarizes existing knowledge on the topic. This is also a good place to discuss the potential implications of the research. The researcher can identify gaps in the literature and suggest areas for further research.
  • Conclusion or discussion section : The conclusion or discussion section is where the researcher summarizes the findings of the study and interprets their meaning. This is a good place to discuss the implications of the research and its potential impact on the field.

Advantages of Implications in Research

Implications are an important part of research that can provide a range of advantages. Here are some of the key advantages of implications in research:

  • Practical applications: Implications can help researchers to identify practical applications of their research findings, which can be useful for practitioners and policymakers who are interested in applying the research in real-world contexts.
  • Improved decision-making: Implications can also help decision-makers to make more informed decisions based on the research findings. By clearly identifying the implications of the research, decision-makers can understand the potential outcomes of their decisions and make better choices.
  • Future research directions : Implications can also guide future research directions by highlighting areas that require further investigation or by suggesting new research questions. This can help to build on existing knowledge and fill gaps in the current understanding of a topic.
  • Increased relevance: By highlighting the implications of their research, researchers can increase the relevance of their work to real-world problems and challenges. This can help to increase the impact of their research and make it more meaningful to stakeholders.
  • Enhanced communication : Implications can also help researchers to communicate their findings more effectively to a wider audience. By highlighting the practical applications and potential benefits of their research, researchers can engage with stakeholders and communicate the value of their work more clearly.

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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.

Discussion section flow chart

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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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 .

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 …

Discussion section example

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Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

  • James Shaw 1 , 13 ,
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BMC Medical Ethics volume  25 , Article number:  46 ( 2024 ) Cite this article

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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

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Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

Clark P, Kim J, Aphinyanaphongs Y, Marketing, Food US. Drug Administration Clearance of Artificial Intelligence and Machine Learning Enabled Software in and as Medical devices: a systematic review. JAMA Netw Open. 2023;6(7):e2321792–2321792.

Article   Google Scholar  

Potnis KC, Ross JS, Aneja S, Gross CP, Richman IB. Artificial intelligence in breast cancer screening: evaluation of FDA device regulation and future recommendations. JAMA Intern Med. 2022;182(12):1306–12.

Siala H, Wang Y. SHIFTing artificial intelligence to be responsible in healthcare: a systematic review. Soc Sci Med. 2022;296:114782.

Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, et al. A large language model for electronic health records. NPJ Digit Med. 2022;5(1):194.

Meskó B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digit Med. 2023;6(1):120.

Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nat Mach Intell. 2019;1(9):389–99.

Minssen T, Vayena E, Cohen IG. The challenges for Regulating Medical Use of ChatGPT and other large Language models. JAMA. 2023.

Ho CWL, Malpani R. Scaling up the research ethics framework for healthcare machine learning as global health ethics and governance. Am J Bioeth. 2022;22(5):36–8.

Yeung K. Recommendation of the council on artificial intelligence (OECD). Int Leg Mater. 2020;59(1):27–34.

Maddox TM, Rumsfeld JS, Payne PR. Questions for artificial intelligence in health care. JAMA. 2019;321(1):31–2.

Dzau VJ, Balatbat CA, Ellaissi WF. Revisiting academic health sciences systems a decade later: discovery to health to population to society. Lancet. 2021;398(10318):2300–4.

Ferretti A, Ienca M, Sheehan M, Blasimme A, Dove ES, Farsides B, et al. Ethics review of big data research: what should stay and what should be reformed? BMC Med Ethics. 2021;22(1):1–13.

Rahimzadeh V, Serpico K, Gelinas L. Institutional review boards need new skills to review data sharing and management plans. Nat Med. 2023;1–3.

Kling S, Singh S, Burgess TL, Nair G. The role of an ethics advisory committee in data science research in sub-saharan Africa. South Afr J Sci. 2023;119(5–6):1–3.

Google Scholar  

Cengiz N, Kabanda SM, Esterhuizen TM, Moodley K. Exploring perspectives of research ethics committee members on the governance of big data in sub-saharan Africa. South Afr J Sci. 2023;119(5–6):1–9.

Doerr M, Meeder S. Big health data research and group harm: the scope of IRB review. Ethics Hum Res. 2022;44(4):34–8.

Ballantyne A, Stewart C. Big data and public-private partnerships in healthcare and research: the application of an ethics framework for big data in health and research. Asian Bioeth Rev. 2019;11(3):315–26.

Samuel G, Chubb J, Derrick G. Boundaries between research ethics and ethical research use in artificial intelligence health research. J Empir Res Hum Res Ethics. 2021;16(3):325–37.

Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021;22(1):1–17.

Teixeira da Silva JA. Handling ethics dumping and neo-colonial research: from the laboratory to the academic literature. J Bioethical Inq. 2022;19(3):433–43.

Ferryman K. The dangers of data colonialism in precision public health. Glob Policy. 2021;12:90–2.

Couldry N, Mejias UA. Data colonialism: rethinking big data’s relation to the contemporary subject. Telev New Media. 2019;20(4):336–49.

Organization WH. Ethics and governance of artificial intelligence for health: WHO guidance. 2021.

Metcalf J, Moss E. Owning ethics: corporate logics, silicon valley, and the institutionalization of ethics. Soc Res Int Q. 2019;86(2):449–76.

Data Protection Act - OFFICE OF THE DATA PROTECTION COMMISSIONER KENYA [Internet]. 2021 [cited 2023 Sep 30]. https://www.odpc.go.ke/dpa-act/ .

Sharon T, Lucivero F. Introduction to the special theme: the expansion of the health data ecosystem–rethinking data ethics and governance. Big Data & Society. Volume 6. London, England: SAGE Publications Sage UK; 2019. p. 2053951719852969.

Reisman D, Schultz J, Crawford K, Whittaker M. Algorithmic impact assessments: a practical Framework for Public Agency. AI Now. 2018.

Morgan RK. Environmental impact assessment: the state of the art. Impact Assess Proj Apprais. 2012;30(1):5–14.

Samuel G, Richie C. Reimagining research ethics to include environmental sustainability: a principled approach, including a case study of data-driven health research. J Med Ethics. 2023;49(6):428–33.

Kwete X, Tang K, Chen L, Ren R, Chen Q, Wu Z, et al. Decolonizing global health: what should be the target of this movement and where does it lead us? Glob Health Res Policy. 2022;7(1):3.

Abimbola S, Asthana S, Montenegro C, Guinto RR, Jumbam DT, Louskieter L, et al. Addressing power asymmetries in global health: imperatives in the wake of the COVID-19 pandemic. PLoS Med. 2021;18(4):e1003604.

Benatar S. Politics, power, poverty and global health: systems and frames. Int J Health Policy Manag. 2016;5(10):599.

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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JS led the writing, contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. CA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. PYC contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AE contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JWG contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AH contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DJ contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. KL contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DP contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. EV contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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Neurophysiological insights into sequential decision-making: exploring the secretary problem through ERPs and TBR dynamics

  • Dor Mizrahi 1 ,
  • Ilan Laufer 1 &
  • Inon Zuckerman 1  

BMC Psychology volume  12 , Article number:  245 ( 2024 ) Cite this article

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Decision-making under uncertainty, a cornerstone of human cognition, is encapsulated by the “secretary problem” in optimal stopping theory. Our study examines this decision-making challenge, where participants are required to sequentially evaluate and make irreversible choices under conditions that simulate cognitive overload. We probed neurophysiological responses by engaging 27 students in a secretary problem simulation while undergoing EEG monitoring, focusing on Event-Related Potentials (ERPs) P200 and P400, and Theta to Beta Ratio (TBR) dynamics.

Results revealed a nuanced pattern: the P200 component’s amplitude declined from the initial to the middle offers, suggesting a diminishing attention span as participants grew accustomed to the task. This attenuation reversed at the final offer, indicating a heightened cognitive processing as the task concluded. In contrast, the P400 component’s amplitude peaked at the middle offer, hinting at increased cognitive evaluation, and tapered off at the final decision. Additionally, TBR dynamics illustrated a fluctuation in attentional control and emotional regulation throughout the decision-making sequence, enhancing our understanding of the cognitive strategies employed.

The research elucidates the dynamic interplay of cognitive processes in high-stakes environments, with neurophysiological markers fluctuating significantly as participants navigated sequential choices. By correlating these fluctuations with decision-making behavior, we provide insights into the evolving strategies from heightened alertness to strategic evaluation. Our findings offer insights that could inform the use of neurophysiological data in the development of decision-making frameworks, potentially contributing to the practical application of cognitive research in real-life contexts.

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Introduction

The secretary problem [ 1 , 2 ], a pivotal element in optimal stopping theory [ 3 , 4 , 5 , 6 ], serves as a metaphor for high-stakes decisions under uncertainty. It presents a situation where a decision-maker must choose the optimal time to commit, with each decision eliminating future options. This scenario transcends recruitment, paralleling significant life decisions in diverse contexts such as real estate, finance, and personal relationships. Here, the key challenge lies in the trade-off between acquiring adequate information and the urgency of decision-making, with the risk of missed opportunities juxtaposed against the cost of premature commitment.

This optimal stopping problem is emblematic of decision-making under uncertainty, where the decision-maker faces sequential choices without the ability to revert to previous ones. This framework is particularly resonant in an era rich with information yet constrained by human cognitive capacities [ 7 ]. Despite the plethora of data available, individuals often resort to heuristic strategies to manage the cognitive and metabolic costs associated with information processing and decision-making [ 7 ].

Our study explores the neurophysiological dimensions of decision-making within the context of the secretary problem, addressing a gap in neuroscientific research, particularly outside of visual search contexts [ 7 , 8 , 9 ]. We focus on how neurophysiological markers, especially Event-Related Potential (ERP) components P200 and P400 [ 10 , 11 , 12 , 13 ] and the Theta to Beta Ratio (TBR) dynamics [ 14 , 15 ], vary in response to sequential alternatives and influence decision-making processes, due to their relevance to focused attention, cognitive evaluation, and stress, respectively.

The P200 component, associated with attention and orientation toward stimuli, emerges around 200 ms after stimulus presentation. Its amplitude serves as an indicator of attentional engagement, which is pivotal for the initial interaction with the choices presented [ 10 , 16 , 17 ]. The P400 component, manifesting roughly 400 ms post-stimulus, is linked to the cognitive effort and evaluation necessary for processing decision options. This component reflects the depth of cognitive processing engaged to assess each option, highlighting its importance in the decision-making process [ 18 ]. Additionally, the Theta to Beta Ratio (TBR) is employed as a measure of stress and cognitive load. An elevated TBR suggests heightened stress levels and cognitive demand, providing insights into the psychological strain experienced by participants during the decision-making task [ 14 , 15 , 19 , 20 ]. By incorporating these neurophysiological markers, our study aims to elucidate how individuals allocate attention, engage in cognitive evaluation, and manage stress throughout the decision-making process, contributing to a more comprehensive view of the cognitive and emotional dynamics involved.

The backdrop of our research includes theoretical models that allude to the complexities of the secretary problem. The Adaptive Gain Model [ 6 , 21 , 22 ], for example, illustrates how decision-makers adaptively balance sensory input against urgency signals in varying reliability scenarios, reflecting the dynamic nature of decision-making. Similarly, Linear Threshold Models (LTMs) [ 23 , 24 ] suggest linearly increasing decision thresholds, offering insight into strategy formation in complex decision scenarios. These models, provide a broader context to understand decision-making strategies that range from linear evidence accumulation to adaptive weighting of information.

Additionally, we draw on recent findings by Sin et al. [ 25 ], who emphasize the role of subjective valuation in decision-making. Their research on physiological markers like pupil dilation correlates with internal decision-making challenges and individual choices, highlighting the importance of subjective valuation processes. Specifically, our research aims to explore how these neurophysiological markers inform our understanding of decision-making behavior, particularly in the dynamic setting of sequential offers characteristic of the secretary problem. By examining changes in ERPs and TBR in response to these choices, we seek to unveil the cognitive and emotional dynamics underpinning decision-making strategies in high-stakes, dynamically evolving environments.

To elucidate the neurophysiological processes underlying sequential decision-making in the secretary problem, we formulated specific hypotheses concerning the behavior of these key markers: the P200 and P400 components, and the TBR dynamics. For the P400 component, our hypothesis centers on its role in the cognitive evaluation of each offer. We predict a gradual increase in amplitude across the task, with peaks corresponding to moments of deeper cognitive processing and decision complexity. This pattern would reflect the participants’ engagement in more detailed evaluations as they progress through the offers, particularly as they approach the task’s end and the weight of each decision grows.

Regarding TBR dynamics, we hypothesize an initial variation in response to the task’s onset, reflecting the cognitive and emotional response to beginning the decision-making process. As participants proceed, we anticipate fluctuations in TBR levels, which could reflect changes in cognitive load and emotional regulation. Specifically, we expect these fluctuations to mirror the task’s demands at different stages, with potential increases indicating heightened cognitive effort or stress, particularly in the latter stages of decision-making.

In summary, our study seeks to contribute to the broader understanding of decision-making processes by integrating neurophysiological insights with the conceptual backdrop provided by these theoretical models. Our goal is to deepen the comprehension of the secretary problem, encouraging further detailed and practical research in various decision-making environments.

Participants

The study involved 27 fourth-year students (16 females) from a senior engineering program, ranging in age from 20 to 35 (average age of 24.25 with a standard deviation of 2.0673). Participants were selected for their right-handedness and absence of neurological issues to minimize variability in neurophysiological responses. Informed consent was obtained from each participant, and the appropriate institutional review board approved the study protocol.

Task design

Participants engaged in six blocks of the secretary problem task, each consisting of 20 monetary offers. These offers, represented as sums of money displayed on-screen, required participants to either accept or reject each one using designated keys (‘y’ for ‘yes’ to accept and ‘N’ for ‘no’ to reject). Simulating the role of apartment sellers, participants were aware that they could neither see future offers nor revisit past ones. The primary objective was to select the highest offer in each block to maximize potential earnings, with their actual compensation (ranging from 100 to 150 NIS) reflecting their performance in the task. It is important to note that if a participant did not choose any offer within a block, it was considered as if they had accepted the last offer presented to them. This approach ensures that every block results in a definitive decision, aligning with the task’s parameters. Details of the offers are documented in Table A1 in Appendix A.

This table is adapted from the study conducted by Hsiao and Kemp [ 26 ]. However, in the design of Blocks 2, 4, and 6, an experimental modification was applied where the original bid values from Hsiao and Kemp [ 26 ] were increased tenfold. This substantial augmentation of bid values was strategically employed to inject a broadened range of economic scenarios, thereby disrupting any potential conditioning to a constrained set of bid amounts. The purpose of this augmentation was to examine participant behavior in the face of significantly varied bid levels, providing insight into how participants’ valuation strategies adjust when presented with a wider spectrum of potential selling prices for an asset they ‘own’, such as an apartment. The application of increased bid values in alternate blocks is poised to yield insights into the influence of monetary scale on strategy development and risk evaluation. It allows for an assessment of whether, and how, decision-making in the secretary problem adjusts in the presence of larger potential gains, which may mimic real-world economic decision scenarios more closely than a constrained value range.

Experimental procedure

The experimental block was structured to reflect the secretary problem, an optimal stopping problem, which allowed us to scrutinize participant decision-making under uncertainty. Each block’s flow, represented in Fig.  1 , depicts the sequence of screens and decisions: from the initial welcome screen to the series of offers, punctuated by waiting screens, culminating in the participant’s final decision:

figure 1

Sequential flow diagram of a single block in the Secretary Problem experiment, depicting offer presentation and participant decision points

Below, the procedural stages within a single block are outlined as depicted in Fig.  1 , encompassing the initial orientation, the intermittent waiting periods, the offer evaluations, and the iterative feedback loop that triggers the cycle to recommence following each decision.

Welcome Screen : This initial display set the context for the upcoming task.

Waiting Screen : Displayed for a randomized duration between 200 and 500 ms, this screen served as a transitional phase, ensuring unpredictability in the timing of the subsequent offer presentation.

Offer Screen : Here, participants were presented with monetary offers, with no time restriction imposed for making decisions. This stage remained active until a decision (accept or reject) was made by the participant.

Feedback Loop : If an offer was rejected and the session had not yet concluded, the procedure reverted to the Waiting Screen, maintaining the cycle of decision-making.

Incentivization and standardization

The participants’ compensation ranged from 100 to 150 NIS, directly corresponding to their success in selecting the highest offer within the tasks. This tiered incentive system was intended to mirror realistic reward scenarios and to foster a high level of engagement with the task by making the remuneration dependent on the accuracy of their choices. Uniformity across the experiment was maintained by providing all participants with the same sequence of offers.

Preparation and training sessions

Before beginning data collection, participants underwent a thorough briefing and were equipped with an EEG cap. To ensure they were comfortable with the task and the experimental environment, two training sessions were conducted. These sessions, held without EEG recording, were important for familiarizing participants with the task.

EEG recording

EEG signals were captured using a 16-channel active EEG amplifier (e.g., USBAMP, by g.tec, Austria) at 512 Hz, adhering to the 10–20 international system. Electrode impedance was maintained below 5 Kohm, monitored using OpenVibe software. Analysis focused on six frontal and prefrontal electrodes (Fp1, F7, Fp2, F8, F3, F7), as these areas correlate with cognitive processing.

Data preprocessing involved a 1–30 Hz FIR bandpass filter and Independent Component Analysis (ICA) to segregate neural activity and artifacts, utilizing EEGLAB’s algorithm for artifact removal, as described by Delorme and Makeig (2004) [ 27 ]. In this process, we identified and removed eye movements and blinks, muscle activity from facial and neck muscles, and environmental electrical noise. The latter, often stemming from power lines, was addressed using a notch FIR filter specifically designed to remove such 50 Hz line noise artifacts.

The following steps in our protocol, illustrated in Fig.  2 , involved applying baseline correction 200 ms prior to the onset of the offer screen and implementing average referencing to further enhance the refinement of the EEG data. Downsampling to 64 Hz was strategically reserved for the computation of the TBR, which does not necessitate the high temporal resolution required for the accurate identification of P200 and P400 components’ peak amplitudes. Specifically, the original sampling rate of 512 Hz was preserved for P200 and P400 to accurately capture the temporal dynamics of these faster ERP components. The downsampled data, suitable for assessing the lower-frequency Theta and Beta bands involved in TBR, allowed us to perform this specific analysis with optimal resolution and minimal loss of information.

figure 2

Schematic of EEG preprocessing and analysis pipeline

ERP analysis and statistical approach

Our study focused on ERPs, particularly the P200 and P400 peaks, to understand the cognitive processes [ 11 ] during decision-making in the secretary problem. We analyzed these ERP components for the first, middle, and final offers within each sequence. The first offer refers to the initial one in the sequence, the last offer is the one ultimately chosen by participants, and the middle offer is calculated as the median offer within the range from the first to the last. In cases with an uneven number of offers, the middle offer was identified as the median offer ((total number of offers from first to last + 1) / 2).

For each participant, we identified the maximum amplitude point in each ERP trace within each epoch, corresponding to the initial, middle, and last offers. This approach allowed us to capture the ERP responses specific to each decision point in every block, rather than averaging across all six blocks.

Specifically, we adopted the temporal Regions of Interest (ROIs) as outlined by [ 11 ] for the analysis of ERP components, specifically focusing on the P200 and P400. Following their methodology, we identified the maximum peak within these ROIs and then conducted our amplitude calculations within a 60 ms interval surrounding these peaks. This approach closely aligns with the methodology employed by Kornmeier et al. [ 11 ], who focused on a ± 20 ms interval surrounding the peaks for their analysis. While Kornmeier et al. [ 11 ] averaged the amplitude within these intervals, our study differentiated itself by computing the relative energy across the 60 ms window, allowing for a quantification of the ERP signal’s power within the ROIs.

Kornmeier et al. included two distinct temporal ROIs in their ERP analysis, targeting the P400 between 300 ms and 650 ms, and an earlier P200 effect between 100 ms and 300 ms post-stimulus. We mirrored this approach in selecting our intervals of 170 to 230 ms for the P200 and 370 to 430 ms for the P400 around the respective peaks. With a 512 Hz sampling rate, these intervals of 60 ms surrounding each peak were standardized to correspond to 32 samples, enabling energy calculations surrounding the corresponding peaks.

The decision to utilize frontal and prefrontal electrode placements drew upon the precedent set by visual decision-making studies such as [ 28 ], which identified significant ERP activity, including the P400 in frontal regions in the context of a visual Go/ NoGo task. This alignment supports the relevance of our electrode configuration in investigating the cognitive processes mediated by the frontal lobes during decision-making tasks [ 28 ].

An essential step in our analysis was establishing a baseline for energy calculations. By assuming a uniform distribution of energy across the entire EEG signal, we calculated a baseline reference value as Relative Energy Baseline (RE Baseline ) = 32/512 = 0.0625, which is equivalent to 6.25% [ 13 ]. The Relative Energy (RE) of the P200 and P400 peaks was calculated by integrating the square of the voltage across the time window corresponding to these ERP components. This provides a measure of the total energy within the segment without baseline correction. The uniform RE Baseline value of 6.25% was then used as a reference to compare the relative energy increase during the P200 and P400 peaks, thereby allowing for the assessment of ERP activations in relation to the baseline EEG activity. To evaluate the impact of different variables on these ERP components, we conducted a two-way repeated-measures ANOVA. This analysis was key to understanding how the offer’s position (initial, middle, end) and the ERP type (P200 or P400) affected the energy patterns observed in each individual epoch.

Discrete wavelet transform computation for theta and beta band energy assessment

In this study, the TBR was utilized as a critical metric for analyzing cognitive processing across the decision-making sequence [ 14 , 15 ]. For each participant, the average TBR was computed for each of the six blocks, aggregating all epochs from the first to the selected offer. Consequently, each participant contributed six data points, one from each block, to the analysis.

The Discrete Wavelet Transform (DWT), adhering to established methodologies (referenced in studies [ 29 , 30 ]), was pivotal in our TBR computation. The DWT’s multiscale representation approach, as detailed in [ 31 ], captures various dimensions of the EEG signal. This process incorporated dual digital filters at each stage: a high-pass filter ( 𝑔 ( 𝑛 )) and a low-pass filter ( ℎ ( 𝑛 )). Post-filtering, a downsampler with a factor of 2 was employed to adjust the time resolution appropriately. We utilized a 3-level DWT, processing input signals at a 64 Hz sampling rate, effectively isolating coefficients corresponding to the four primary EEG frequency bands. The TBR for each epoch was calculated by measuring the average energy ratio between the Theta and Beta bands (Theta / Beta). Detailed methodology for this calculation can be found in Mizrahi et al. [ 32 , 33 ].

To further interpret the distribution of these data points, representing averaged TBR for a sequence of offers in each block, a polynomial modeling approach [ 34 ] was applied. This method aimed to accurately approximate the TBR values, with each point reflecting the average TBR from the initial to the final accepted offer within a block. Polynomial models of orders one to five were evaluated based on their P-value and R² statistics to determine the most representative model. This analysis allowed us to identify the polynomial order that most accurately mirrored the data trend, offering insightful perspectives on how TBR varied across different stages of offer selection.

In the next two subsections, we will detail the modulation of P200 and P400 across crucial points in task progression and will elucidate TBR modulation against the position of the accepted offer (last offer) in the task. The ERP components P200 and P400 were specifically assessed at three key stages: the first offer, the median offer, and the final offer received by the participant. These stages correspond to the initial engagement, the development of decision-making strategy, and the final decision point within the secretary problem, respectively. The first offer introduces the participant to the task demands, the median offer represents an intermediary assessment phase, and the final offer captures the participant’s commitment to a decision. In conjunction with ERP data, the TBR was analyzed in relation to the position of the final accepted offer to explore its association with stress levels at the decisive moment of the task. By correlating these neurophysiological markers with task progression, this methodological framework aims to provide insights into the cognitive and emotional aspects that inform decision-making throughout the secretary problem. Data from each of the ERP components (P200 and P400) and TBR values were aggregated across all six experimental blocks and include the data from all 27 participants.

Dynamics of ERP components across decision stages

Figure  3 displays the average relative energies (RE) for the P200 and P400 ERP components at different stages of the secretary problem, with the baseline RE set at 6.25%, as indicated by the dashed line. This baseline signifies a uniform distribution of energy throughout the EEG signal. Figure  3 displays an interaction graph comparing the amplitudes of two Event-Related Potentials (ERPs), P200 and P400, at three pivotal stages of the decision-making task within the secretary problem context. The red trace (P200) begins at a higher amplitude on the first offer, indicating initial cognitive engagement. It then descends to a lower amplitude at the middle offer, suggesting a potential decrease in attentional focus, before ascending again at the last offer selected. In contrast, the amplitude of the P400 component increases from the first to the middle offer, with this upward trend continuing, albeit at a reduced pace, towards the last offer. This pattern suggests a gradual but not uniform intensification of cognitive processing across the task.

figure 3

Comparative ERPs across offers, showing P200 and P400 amplitudes at the first, middle, and last selected offers, with standard deviation lines and a reference line indicating the RE Baseline

Statistical validation, as evidenced by the results of the ANOVA, corroborates the patterns observed in Fig.  3 . The analysis revealed significant main effects of offer position on the amplitudes of the ERP components (F(2, 966) = 184.007, p  < .00001), aligning with the observed shifts in the P200 and P400 traces at different stages of the decision-making task. Specifically, the initial heightened engagement indicated by the P200 amplitude at the first offer, the decreased attentional focus at the middle offer, and the renewed cognitive assessment at the final offer are statistically significant. Similarly, the progression of the P400 component, starting at a lower amplitude and peaking at the middle offer, followed by a decrease at the final offer, is also supported by the statistical analysis.

Furthermore, the ANOVA indicates a significant effect of the type of ERP component on cognitive processing (F(1, 966) = 1831.4, p  < .00001), reflecting the distinct roles of P200 and P400 in the participants’ cognitive strategies during the task. Notably, a significant interaction effect (F(2, 966) = 750.11, p  < .00001) highlights the complex interplay between the position of the offer and the specific ERP component. This finding underscores the interdependent influence of these factors on the cognitive processes involved in the secretary problem, suggesting that participants employ shifting cognitive strategies as they progress through the task.

Figure  4 offers a visual complement to these findings by presenting the box plots that demonstrate the median and interquartile range for ERP amplitudes of P200 and P400 at three decision-making stages. At the first offer, both P200 and P400 demonstrate the widest range of responses, with P200 showing a notably higher median amplitude. In the context of the secretary problem, the initial offer’s broader range of responses and P200’s higher median amplitude could reflect the exploratory cognitive efforts as participants establish an initial framework for value assessment. The significant difference between P200 and P400 (Estimate: 0.59, 95% CI [1.13, 1.67], p  < .0001) indicates distinct neural processing as participants encounter the first decision point, which is critical for calibrating their approach to the uncertainty inherent in the task.

figure 4

Box plots illustrating median amplitudes and interquartile ranges of P200 and P400 ERPs at the first, middle, and last selected offers in a secretary problem task

At the middle offer stage, the box plot reveals a significant decrease in the median amplitude of P200, alongside a notably narrower spread, evidencing a change in the component’s response from the initial offer. The post hoc test supports this observation, showing a significant difference (Estimate: -9.34, 95% CI [-8.80, -8.26], p  < .0001). P400’s median amplitude at this stage is observed to be larger compared to P200, with both components exhibiting a reduced variability from their initial responses.

At the final offer stage, data from the final offer stage show an increase in P200’s median amplitude and a broadening of the interquartile range. P400’s median amplitude is also noted to increase slightly, maintaining a substantially higher median than P200. The statistical analysis highlights a significant difference in response at the final offer stage (Estimate: -6.86, 95% CI [-6.32, -5.79], p  < .0001).

These observations indicate distinct patterns of ERP component responses at key stages of the decision-making task. A decrease in P200’s amplitude and variability is noted at the middle offer stage, followed by an increase at the final offer stage. P400 exhibits an overall increase in median amplitude from the initial to the final stage, with reduced variability across stages.

TBR dynamics in the secretary problem: indicators of mental stress and decision efficiency

Figure  5 presents a scatter plot where the individual TBR values, denoted by the small blue ‘x’ marks, are plotted against the sequence number of offers received. A third-order polynomial fit curve, shown in red, captures the overall trend in the decision-making process of the secretary problem. The green ‘X’ marks represent the average TBR value for each offer. The plot reveals an initial phase of lower TBR values, an ascent to peak TBR values slightly after the optimal stopping point—indicated by the dashed vertical line—and a subsequent decline as the offer sequence progresses. This pattern suggests a correlation between the TBR values and the changing levels of executive control, potentially reflecting the different stress levels experienced by participants throughout the phases of the task.

figure 5

Scatter plot of TBR values by offer sequence number with third-order polynomial fit highlighting decision-making trends in the secretary problem

Table  1 provides an analysis of polynomial fits to elucidate the relationship between TBR values and the sequence of offers. It is observed that the third-order polynomial fit, with an R² value of 0.539, offers a substantial improvement in explaining the variance in TBR values compared to the first and second-order fits. While the fourth and fifth-order fits yield a marginally higher R² value of 0.548, the third-order fit is favored due to its balance between simplicity and explanatory power, aligning with the principle of parsimony and avoiding overfitting that higher-order models may entail.

Our study explores the neurophysiological elements of decision-making within the secretary problem, focusing on ERP components P200 and P400, and TBR dynamics. We aim to elucidate cognitive processes and neural markers critical in sequential decision-making under uncertainty.

Neurophysiological insights into sequential decision-making: integrating ERP and TBR findings

In examining the P200 component during the secretary problem task, we observed a distinctive pattern correlating with the sequence of offers. Initially, there was a marked decrease in P200 amplitude from the first to the middle offers, suggesting a reduction in attentional focus as participants adjusted to the task. However, this trend reversed at the final offer, where a notable increase in amplitude indicated renewed cognitive engagement. This resurgence suggests heightened processing due to the increased significance of the concluding decision. This pattern reveals a more complex cognitive process than just the transition towards a ‘satisficing’ strategy, as posited by Brera and Fu [ 4 ]. At first, the elevated P200 amplitude suggests intense scrutiny of early offers. As the task proceeds, the decrease in amplitude at middle offers implies a shift to a ‘satisficing’ approach, where participants accept offers meeting certain acceptable standards rather than only the best. This strategy adapts to task constraints and the need for timely decision-making. However, the increased P200 amplitude at the final offer suggests a strategic reassessment, possibly as participants realize the finality of their choice. This indicates a re-evaluation of their decision against the ‘satisficing’ threshold, demonstrating flexibility in their cognitive approach.

The reduced variability in P200 amplitude at the middle offers, seemingly at odds with the individualistic nature of ‘satisficing,’ suggests a collective adjustment in cognitive engagement due to task structure and constraints. While participants may have personal satisficing thresholds, the task’s design leads to a convergence in cognitive responses. This observation underscores the adaptive nature of decision-making, evolving with the task’s progression and contextual demands.

The P400 component displayed a consistent increase in amplitude throughout the task, reflecting intensifying cognitive effort and strategic evaluation. The significant rise during the middle phase of the task implies an integration of sequential information and adjustment of expectations [ 12 ]. This finding is further supported by Hsiao and Kemp’s [ 26 ] research on incentive structures in decision-making. Additionally, our interpretation considers the role of internal decision thresholds, akin to ‘internal advice,’ influenced by task incentivization, as illustrated by Liu et al. [ 5 ]. Participants’ evolving P400 responses may reflect cognitive adjustments to these internal benchmarks and the task’s incentive structure.

TBR dynamics and ERP components: cognitive strategies and emotional regulation

The interplay between TBR dynamics and ERP components sheds light on the cognitive strategies and emotional regulation associated with decision-making in the secretary problem. Lower initial TBR values, which suggest greater executive control and align with findings by Angelidis et al. [ 15 ] of enhanced attentional control and reduced stress, set the stage for participants’ strategic information processing. As participants approach the theoretical optimal stopping point, TBR values increase, potentially reflecting heightened cognitive and emotional stress due to the impending necessity of making a critical decision. This rise in stress levels may manifest as an adaptive response, in line with the Adaptive Gain Model proposed by Tickle et al. [ 6 ], whereby participants must balance the processing of incoming sensory information with the urgency of making timely decisions. The subsequent reduction in TBR values after this peak could signify a diminution of both cognitive and emotional stress, marking a return to baseline levels of cognitive control.

Neurophysiology and decision models: conservative choices to digital strategies

Drawing on the insights of Bearden et al. [ 3 ] and Babaioff et al. [ 35 ], our study’s neurophysiological data suggest a tendency toward conservative, risk-averse strategies in decision-making. This inclination is particularly reflected in the observed patterns of the ERP components and TBR dynamics. For instance, the P200 component’s fluctuating amplitude—initially high, decreasing in the middle, and then increasing at the end of the offer sequence—might indicate a cautious approach as participants evaluate each offer. The initial heightened response suggests careful consideration of early options, while the subsequent reduction could imply a strategic withdrawal of attention from less favorable options, aligning with a risk-averse strategy that avoids premature commitments.

Similarly, the consistent increase in the P400 component’s amplitude throughout the task signals a progressive, in-depth engagement in cognitive processing and strategic evaluation. This pattern could be indicative of participants increasingly scrutinizing each offer’s potential risks and benefits as they move through the sequence, a characteristic of risk-averse behavior where decisions are made more meticulously over time.

The TBR dynamics further complement this interpretation. The rise in TBR values, suggesting heightened cognitive engagement with reduced stress, followed by a decline as participants approach their final decision, aligns with a cautious and measured approach to decision-making. Such a pattern might reflect an increasing awareness of the narrowing options and the consequential nature of the impending final choice, characteristic of a risk-averse decision-making style.

Together, these neurophysiological markers might indicate that participants were exercising a cautious, conservative approach in their decision-making process. This approach, characterized by careful evaluation and minimization of potential loss, has significant implications for designing digital interfaces and decision-making systems. Particularly in online environments where decisions are irreversible, understanding these risk-averse cognitive strategies can inform the development of systems that support users in navigating complex decision-making scenarios effectively.

Implications, limitations, and future directions

Our study’s findings offer relevant implications for several fields, particularly financial trading and medical diagnosis, where the stakes are high and decisions must be made under uncertainty. In financial trading, understanding the neurophysiological underpinnings of decision-making could lead to the development of tools that help traders better manage stress and cognitive overload, potentially leading to more informed and balanced investment decisions. Similarly, in medical diagnosis, our insights could assist in creating decision support systems that aid physicians in processing complex information under time pressure, enhancing diagnostic accuracy and patient outcomes.

Furthermore, our research holds promise for the development of advanced decision support systems across various domains. By integrating neurophysiological insights into these systems, we can create interfaces that are more attuned to the users’ cognitive and emotional states, potentially leading to more intuitive and effective decision-making tools. Such systems could be particularly beneficial in scenarios where decisions have to be made quickly and with limited information, as they could provide real-time feedback based on the users’ neurophysiological data.

Moreover, our study also highlights the potential for integrating neurophysiological data, such as EEG patterns observed in decision-making scenarios, into artificial intelligence (AI) applications in psychological research and care. This integration could lead to the development of AI-driven decision-support systems that are tailored to respond to the psychological state of users, especially in high-pressure decision-making environments. Our findings suggest that AI, equipped to analyze neurophysiological markers like ERPs and TBR, could significantly enhance decision-making effectiveness in areas such as financial trading and medical diagnosis. This points to a promising intersection of AI and cognitive neuroscience, indicating a future research direction focused on developing AI models that utilize diverse neurophysiological data for practical, real-world applications in psychology and decision-making support.

We recognize the limitations of our study, notably the homogeneity of our participant pool, primarily composed of fourth-year engineering students, and the controlled nature of the experimental setting. These factors constrain the generalizability of our findings. Future research should strive for greater diversity in participant demographics to cover a broader spectrum of cognitive styles and decision-making behaviors. Moreover, incorporating more complex, real-world decision-making scenarios will not only enhance the ecological validity of our findings but also provide deeper insights into the functionality of neurophysiological markers in everyday decision-making processes.

Additionally, our focus on frontal and prefrontal electrodes to capture ERP components P200 and P400 has illuminated the vital roles these brain regions play in decision-making. However, this approach has also limited our exploration of lateralization effects across a broader area of the scalp, potentially overlooking further insights into cognitive strategies employed during sequential decision-making. To address these concerns and broaden the scope of our neurophysiological investigations, we propose an integrated approach for future research.

Future studies could benefit from refining our focus on electrode data analysis to include a comprehensive examination of data from electrodes covering posterior and parietal regions, in addition to the frontal and prefrontal areas. This expanded analysis aims to enhance our understanding of the spatial distribution of cognitive processes in decision-making and allow for a more detailed exploration of lateralization effects across the scalp. Overcoming the spatial resolution limitations of traditional EEG is also critical. Employing high-density EEG systems and integrating findings from fMRI could provide a more nuanced examination of lateralization effects and cognitive process mapping across the brain. This method could offer richer insights into the neural substrates underpinning decision-making tasks. Furthermore, considering the significant impact of physiological and psychological states on decision-making, incorporating alpha band analysis as a complementary metric could prove beneficial. This addition would enable a broader investigation into the interplay between arousal and cognitive processes, enriching our understanding of how these factors interact during decision-making tasks.

In conclusion, our study contributes to the broader understanding of decision-making research, linking neurophysiological data with theoretical and practical applications. Our study aims to contribute to bridging the gap between academic research and real-world applications, hoping that our findings will inspire further exploration and the development of effective support tools and strategies for decision-making in high-stakes environments.

Data availability

All the experimental data, which includes the players’ electrophysiological recordings and the corresponding resource allocation logs, are stored on the servers of Ariel University. The data can be obtained by request from one of the authors. All the experimental data, which includes the players’ electrophysiological recordings and the corresponding coordination logs, are stored on the servers of Ariel University. The data can be obtained by request from The IRB member, Dr. Chen Hajaj ([email protected]) or from one of the authors (Dor Mizrahi - [email protected], Ilan Laufer - [email protected], Inon Zuckerman - [email protected]).

Ferguson TS. Who solved the secretary problem? Stat Sci. 1989;4:282–9.

Google Scholar  

Skarupski M. Secretary Problem with possible errors in Observation. Mathematics. 2020;8:1639.

Article   Google Scholar  

Bearden JN, Murphy RO, Rapoport A. A multi-attribute extension of the secretary problem: theory and experiments. J Math Psychol. 2005;49:410–22.

Brera R, Fu F. The satisficing secretary problem: when closed-form solutions meet simulated annealing. arXiv Prepr. 2023;arXiv:2302.

Liu X, Milenkovic O, Moustakides GV. A combinatorial proof for the secretary problem with multiple choices. arXiv Prepr. 2023;arXiv:2303.

Tickle H, Tsetsos K, Speekenbrink M, Summerfield C. Human optional stopping in a heteroscedastic world. Psychol Rev. 2023;130:1.

Article   PubMed   Google Scholar  

Browne GJ, Walden EA. Stopping information search: an fMRI investigation. Decis Support Syst. 2021;143.

Anderson EJ, Mannan SK, Rees G, Sumner P, Kennard C. Overlapping functional anatomy for working memory and visual search. Exp Brain Res. 2010;200:91–107.

Bourke P, Brown S, Ngan E, Liotti M. Functional brain organization of preparatory attentional control in visual search. Brain Res. 2013;1530:32–43.

Carretié L, Martín-Loeches M, Hinojosa JA, Mercado F. Emotion and attention interaction studied through event-related potentials. J Cogn Neurosci. 2001;13:1109–28.

Kornmeier J, Wörner R, Bach M. Can I trust in what I see? EEG evidence for a cognitive evaluation of perceptual constructs. Psychophysiology. 2016;53:1507–23.

Dien J, Michelson CA, Franklin MS. Separating the visual sentence N400 effect from the P400 sequential expectancy effect: cognitive and neuroanatomical implications. Brain Res. 2010;1355:126–40.

Zuckerman I, Mizrahi D, Laufer I. Attachment style, emotional feedback, and neural processing: investigating the influence of attachment on the P200 and P400 components of event-related potentials. Front Hum Neurosci. 2023;17.

Angelidis A, van der Does W, Schakel L, Putman P. Frontal EEG theta/beta ratio as an electrophysiological marker for attentional control and its test-retest reliability. Biol Psychol. 2016;121:49–52.

Angelidis A, Hagenaars M, van Son D, van der Does W, Putman P. Do not look away! Spontaneous frontal EEG theta/beta ratio as a marker for cognitive control over attention to mild and high threat. Biol Psychol. 2018;135:8–17.

Lijffijt M, Lane SD, Meier SL, Boutros NN, Burroughs S, Steinberg JL, et al. P50, N100, and P200 sensory gating: relationships with behavioral inhibition, attention, and working memory. Psychophysiology. 2009;46:1059–68.

Article   PubMed   PubMed Central   Google Scholar  

Yang CL, Zhang H, Duan H, Pan H. Linguistic focus promotes the ease of discourse integration processes in reading comprehension: evidence from event-related potentials. Front Psychol. 2019;9:2718.

Kornmeier J, Bach M. Object perception: when our brain is impressed but we do not notice it. J Vis. 2009;9:7–7.

Putman P, Verkuil B, Arias-Garcia E, Pantazi I, van Schie C. EEG theta/beta ratio as a potential biomarker for attentional control and resilience against deleterious effects of stress on attention. Cogn Affect Behav Neurosci. 2014;14:782–91.

van Son D, De Blasio FM, Fogarty JS, Angelidis A, Barry RJ, Putman P. Frontal EEG theta/beta ratio during mind wandering episodes. Biol Psychol. 2019;140:19–27.

Cheadle S, Wyart V, Tsetsos K, Myers N, de Gardelle V, Castan SH, et al. Adaptive gain control during human perceptual choice. Neuron. 2014;81:1429–41.

Li V, Michael E, Balaguer J, Summerfield C. Gain control explains the effect of distraction in human perceptual, cognitive, and economic decision making. Proc Natl Acad Sci. 2018;115:E8825–34.

PubMed   PubMed Central   Google Scholar  

Baumann C, Singmann H, Gershman SJ, von Helversen B. A linear threshold model for optimal stopping behavior. Proc Natl Acad Sci. 2020;117:12750–5.

Baumann C, Schlegelmilch R, von Helversen B. Adaptive behavior in optimal sequential search. J Exp Psychol Gen. 2023;152:657.

Sin Y, Seon H, Shin YK, Kwon O-S, Chung D. Subjective optimality in finite sequential decision-making. PLoS Comput Biol. 2021;17:e1009633.

Hsiao Y-C, Kemp S. The effect of incentive structure on search in the secretary problem. Judgm Decis Mak. 2020;15:182–92.

Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134:9–21.

Falkenstein M, Koshlykova NA, Kiroj VN, Hoormann J, Hohnsbein J. Late ERP components in visual and auditory Go/Nogo tasks. Electroencephalogr Clin Neurophysiol Potentials Sect. 1995;96:36–43.

Shensa MJ. The discrete wavelet transform: wedding the a trous and Mallat algorithms. IEEE Trans Signal Process. 1992;40:2464–82.

Jensen A, la Cour-Harbo A. Ripples in mathematics: the discrete wavelet transform. pringer Science & Business Media; 2001.

Hazarika N, Chen JZ, Tsoi AC, Sergejew A. Classification of EEG signals using the wavelet transform. Sig Process. 1997;59:61–72.

Mizrahi D, Laufer I, Zuckerman I. The Effect of Individual Coordination Ability on Cognitive-Load in Tacit Coordination Games. In: Davis F, Riedl R, Brocke J vom, Léger P-M, Randolph A, Fischer T, editors. NeuroIS Retreat 2020. Vienna, Austria; 2020.

Laufer I, Mizrahi D, Zuckerman I. An electrophysiological model for assessing cognitive load in tacit coordination games. Sensors. 2022;22:477.

Wu Y, Yang P. Polynomial methods in statistical inference: theory and practice. Found Trends® Commun Inf Theory. 2020;17:402–586.

Babaioff M, Dinitz M, Gupta A, Immorlica N, Talwar K. Secretary problems: weights and discounts. In: Proceedings of the twentieth annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics; 2009. pp. 1245–54.

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This research for the manuscript titled “Comparative Analysis of ROCKET-Driven and Classic EEG Features in Predicting Attachment Styles” was conducted without any external funding. The authors declare that they did not receive any grants, sponsorships, or financial support from public, private, or not-for-profit sectors for the work presented in this manuscript.

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DM responsible for visualization and implementation of supporting algorithms. IZ and IL supervised the research activity. All authors carried out the stages of conceptualization, design of methodology, data curation, formal analysis, data modeling, model validation, writing, drafting and editing, discussed the results, read and approved the final manuscript and are accountable for all aspects of the work, read, and agreed to the published version of the manuscript.

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Mizrahi, D., Laufer, I. & Zuckerman, I. Neurophysiological insights into sequential decision-making: exploring the secretary problem through ERPs and TBR dynamics. BMC Psychol 12 , 245 (2024). https://doi.org/10.1186/s40359-024-01750-5

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  • Sequential decision-making
  • ERP (event-Related potentials)
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discussion of research findings and application

A qualitative simulation checking approach of programmed grounded theory and its application in workers’ involvement: extending Corbin and Struss’ grounded theory checking mechanism

  • Published: 29 April 2024

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discussion of research findings and application

  • Haoran Wang 1 ,
  • Bin Hu 1 &
  • Yanting Duan 1  

The validity and reliability of a grounded theory research based on interpretivism involves four dimensions: credibility, transferability, dependability, and confirmability. In order to enhance the credibility of a qualitative study, the findings of the grounded theory need to be checked for consistency with reality. Traditional checking approaches lack universal applicability and are difficult for researchers to implement. This paper proposes a qualitative simulation checking approach for programmed grounded theory research, which can enhance the validity and reliability of programmed grounded theory research in terms of credibility, transferability, and dependability dimensions. This approach is not only a more generally applicable checking approach, but also provides a virtual experiment platform for qualitative research. To overcome the deficiencies caused by following a single approach, a checking framework that integrates programmed grounded theory, qualitative simulation checking, and member checking was proposed. The methodology of this paper is validated by applying to a case of sanitation workers’ involvement in the Internet of Things environment.

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Bozionelos, N.: The big five of personality and work involvement. J. Manag. Psychol. 19 (1), 69–81 (2004). https://doi.org/10.1108/02683940410520664

Article   Google Scholar  

Candela, A.G.: Exploring the function of member checking. The Qualit. Rep. 24 (3), 619–628 (2019). https://doi.org/10.46743/2160-3715/2019.3726

Cao, Q., Yu, K., Zhou, L., Wang, L., Li, C.: In-depth research on qualitative simulation of coal miners’ group safety behaviors. Saf. Sci. 113 (3), 210–232 (2019). https://doi.org/10.1016/j.ssci.2018.11.012

Carson, J. S.: Verification and validation: a consultant's perspective. In Proceedings of the 21st conference on Winter simulation. 1989; pp. 552–558

Charmaz, K.: Constructing grounded theory: A practical guide through qualitative analysis. Sage (2006)

Cho, J., Allen, T.: Validity in qualitative research revisited. Qual. Res. 6 (3), 319–340 (2006). https://doi.org/10.1177/1468794106065006

Corbin, J., Strauss, A.: Basics of qualitative research: techniques and procedures for developing grounded theory. Sage publications (2014)

Cypress, B.S.: Rigor or reliability and validity in qualitative research: perspectives, strategies, reconceptualization, and recommendations. Dimens. Crit. Care Nurs. 36 (4), 253–263 (2017)

Dahwa, C.: Adapting and blending grounded theory with case study: a practical guide. Qual. Quant. (2023). https://doi.org/10.1007/s11135-023-01783-9

De Jong, H., Geiselmann, J., Hernandez, C., Page, M.: Genetic network analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics 19 (3), 336–344 (2003). https://doi.org/10.1093/bioinformatics/btf851

Demerouti, E., Bakker, A.B., Nachreiner, F., Schaufeli, W.B.: The job demands-resources model of burnout. J. Appl. Psychol. 86 (3), 499–512 (2001). https://doi.org/10.1037/0021-9010.86.3.499

Eisenberger, R., Armeli, S., Rexwinkel, B., Lynch, P.D.: Rhoades, L: reciprocation of perceived organizational support. J. Appl. Psychol. 86 (1), 42–51 (2001). https://doi.org/10.1037/0021-9010.86.1.42

Eisenhardt, K.M., Graebner, M.E.: Theory building from cases: opportunities and challenges. Acad. Manag. J. 50 (1), 25–32 (2007). https://doi.org/10.5465/amj.2007.24160888

Gioia, D.A., Corley, K.G., Hamilton, A.L.: Seeking qualitative rigor in inductive research: notes on the Gioia methodology. Org. Res. Methods 16 (1), 15–31 (2013). https://doi.org/10.1177/1094428112452151

Glaser, B., Strauss, A.: The discovery of grounded theory: Strategies for qualitative research. Aldine De Gruyter, New York (1976)

Google Scholar  

Glaser, B.: Basics of Grounded Theory Analysis. Sociology Press, Mill Valley, CF (1992)

Glaser, B.: Theoretical sensitivity. Sociology Press, Mill Valley, CA (1978)

Grodal, S., Anteby, M., Holm, A.L.: Achieving rigor in qualitative analysis: the role of active categorization in theory building. Acad. Manag. Rev. 46 (3), 591–612 (2021). https://doi.org/10.5465/amr.2018.0482

Guerrin, F., Dumas, J.: Knowledge representation and qualitative simulation of salmon redd functioning. Part I: qualitative modeling and simulation. Biosystems 59 (2), 75–84 (2001). https://doi.org/10.1016/S0303-2647(01)00100-9

Hansen, S., Baroody, A.J.: Electronic health records and the logics of care: complementarity and conflict in the US healthcare system. Inf. Syst. Res. 31 (1), 57–75 (2020). https://doi.org/10.1287/isre.2019.0875

Hu, B., Xia, N.: Cusp catastrophe model for sudden changes in a person’s behavior. Inf. Sci. 294 (10), 489–512 (2015). https://doi.org/10.1016/j.ins.2014.09.055

Johnson, J.S., Matthes, J.M.: Sales-to-marketing job transitions. J. Market. 82 (4), 32–48 (2018). https://doi.org/10.1509/jm.17.0279

Kanungo, R.N.: Measurement of job and work involvement. J. Appl. Psychol. 67 (3), 341–349 (1982). https://doi.org/10.1037/0021-9010.67.3.341

Karjalainen, M., Sarker, S., Siponen, M.: Toward a theory of information systems security behaviors of organizational employees: a dialectical process perspective. Inf. Syst. Res. 30 (2), 687–704 (2019). https://doi.org/10.1287/isre.2018.0827

Kellogg, K.C., Valentine, M.A., Christin, A.: Algorithms at work: the new contested terrain of control. Acad. Manag. Ann. 14 (1), 366–410 (2020). https://doi.org/10.5465/annals.2018.0174

Khoury, T.A., Shymko, Y., Vermeire, J.: Simulating the cause: how grassroots organizations advance their credibility through the dramaturgical curation of events. Organ. Sci. 33 (4), 1470–1500 (2022). https://doi.org/10.1287/orsc.2021.1489

Kim, D.S., Chung, C.K.: Qualitative simulation on the dynamics between social capital and business performance in strategic networks. J. Distrib. Sci. 14 (9), 31–45 (2016). https://doi.org/10.15722/jds.14.9.201609.31

Kirk J., Miller M. L.: Reliability and validity in qualitative research. Sage publications (1986)

Kleindorfer, G.B., O’Neill, L., Ganeshan, R.: Validation in simulation: various positions in the philosophy of science. Manage. Sci. 44 (8), 1087–1099 (1998). https://doi.org/10.1287/mnsc.44.8.1087

Knight, C., Patterson, M., Dawson, J.: Building work engagement: a Systematic review and meta-analysis investigating the effectiveness of work engagement interventions. J. Organ. Behav. 38 (6), 792–812 (2017). https://doi.org/10.1002/job.2167

Kohnke, E.J., Mukherjee, U.K., Sinha, K.K.: Delivering long-term surgical care in underserved communities: the enabling role of international NPO s as partners. Prod. Oper. Manag. 26 (6), 1092–1119 (2017). https://doi.org/10.1111/poms.12705

Kuipers, B.: Qualitative simulation. Artif. Intell. 29 (3), 289–338 (1986). https://doi.org/10.1016/0004-3702(86)90073-1

Kunze, L., Beetz, M.: Envisioning the qualitative effects of robot manipulation actions using simulation-based projections. Artif. Intell. 247 (6), 352–380 (2017). https://doi.org/10.1016/j.artint.2014.12.004

Lesener, T., Gusy, B., Wolter, C.: The job demands-resources model: a meta-analytic review of longitudinal studies. Work Stress. 33 (1), 76–103 (2019). https://doi.org/10.1080/02678373.2018.1529065

Leung, L.: Validity, reliability, and generalizability in qualitative research. J. Fam. Med. Prim. Care. 4 (3), 324–327 (2015)

Liu, Q., Qu, X., Zhao, D., Guo, Y.: Qualitative simulation of organization quality specific immune decision-making of manufacturing enterprises based on QSIM algorithm simulation. J. Comput. Methods Sci. Eng. 21 (6), 2059–2076 (2021). https://doi.org/10.3233/JCM-215523

Lu, Y., Wang, F., Jia, M., Qi, Y.: Centrifugal compressor fault diagnosis based on qualitative simulation and thermal parameters. Mech. Syst. Signal Process. 81 (12), 259–273 (2016). https://doi.org/10.1016/j.ymssp.2016.03.018

Lubet, S.: Interrogating ethnography: Why evidence matters. Oxford University Press, New York (2017)

Markus, M.L., Robey, D.: Information technology and organizational change: causal structure in theory and research. Manage. Sci. 34 (5), 583–598 (1988). https://doi.org/10.1287/mnsc.34.5.583

Martínez-Miranda, J., Pavón, J.: Modeling the influence of trust on work team performance. SIMULATION 88 (4), 408–436 (2012)

Morse, J.M., Barrett, M., Mayan, M., Olson, K., Spiers, J.: Verification strategies for establishing reliability and validity in qualitative research. Int J Qual Methods 1 (2), 13–22 (2002)

Oh, T.T., Pham, M.T.: A liberating-engagement theory of consumer fun. J. Cons. Res. 49 (1), 46–73 (2022). https://doi.org/10.1093/jcr/ucab051

Oliver, D., Cole, B.M.: The interplay of product and process in skunkworks identity work: an inductive model. Strateg. Manag. J. 40 (9), 1491–1514 (2019). https://doi.org/10.1002/smj.3034

Ropers, D., De Jong, H., Page, M., Schneider, D., Geiselmann, J.: Qualitative simulation of the carbon starvation response in Escherichia coli. Biosystems 84 (2), 124–152 (2006). https://doi.org/10.1016/j.biosystems.2005.10.005

Ruesch, L., Tarakci, M., Besiou, M., Van Quaquebeke, N.: Orchestrating coordination among humanitarian organizations. Prod. Oper. Manag. 31 (5), 1977–1996 (2022). https://doi.org/10.1111/poms.13660

Saadatpour, A., Albert, R.: A comparative study of qualitative and quantitative dynamic models of biological regulatory networks. EPJ Nonlinear Biomed. Phys. 4 (1), 1–13 (2016)

Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., Jinks, C.: Saturation in qualitative research: exploring its conceptualization and operationalization. Qual. Quant. 52 (4), 1893–1907 (2018)

Schaufeli, W.B., Salanova, M., Gonzalez-Roma, V., Bakker, A.B.: The measurement of engagement and burnout: a two sample confirmatory factor analytic approach. J. Happiness Stud. 3 , 71–92 (2002)

Small, M.L.: Causal thinking and ethnographic research. Am. J. Sociol. 119 (3), 597–601 (2013). https://doi.org/10.1086/675893

Sting, F.J., Stevens, M., Tarakci, M.: Temporary deembedding buyer–supplier relationships: a complexity perspective. J. Oper. Manag. 65 (2), 114–135 (2019). https://doi.org/10.1002/joom.1008

Strauss, A.: Qualitative analysis for social scientists. Cambridge university press (1987)

Sutton, R.I., Staw, B.M.: What theory is not. Adm. Sci. Q. 40 (3), 371–384 (1995). https://doi.org/10.2307/2393788

Wiesche, M., Jurisch, M.C., Yetton, P.W., Krcmar, H.: Grounded theory methodology in information systems research. MIS Quarter. 41 (3), 685–701 (2017)

Zhao, X., Hu, B.: Qualitative simulation on staff counterproductive work behaviors based on stochastic catastrophe theory. J. Manag. Sci. China 19 (2), 13–30 (2016)

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Acknowledgements

The authors would like to thank the participants in the study discussed in this paper as well as the managers of the sanitation work in Shenzhen, China.

This work was supported by the [National Natural Science Foundation of China] (grant number [72371110], [71971093], [72132001]) and the [Fundamental Research Funds for the Central Universities] (grant number [2023WKZDJC007]).

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by HW, BH and YD. The first draft of the manuscript was written by HW and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Bin Hu .

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Conflict of interest.

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

The study does not include any procedure which involves danger, harm, distress or discomfort to research participants. Participants requested anonymity, and we only required the identification of their job type as well as their position in our study. We used codes to identify individuals. All participants gave verbal consent for us to disclose the codes representing their personal information and the results of this study.

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Wang, H., Hu, B. & Duan, Y. A qualitative simulation checking approach of programmed grounded theory and its application in workers’ involvement: extending Corbin and Struss’ grounded theory checking mechanism. Qual Quant (2024). https://doi.org/10.1007/s11135-024-01864-3

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A New Model for Studying Social Isolation and Health in People with Serious Mental Illnesses

Researchers have developed a promising new framework for studying the link between social disconnection and poor physical health in people living with serious mental illnesses (SMI). Drawing on published research from animal models and data from the general population, this framework builds on existing social isolation and loneliness models by integrating insights from evolutionary and cognitive theories. This research was supported by the Office of Behavioral and Social Sciences Research and the National Institute of Mental Health.

What were the researchers studying and why?

One of the most challenging aspects of living with SMI is difficulties with social perception, motivation, and social behaviors. These difficulties can lead to social withdrawal and loneliness, outcomes that can contribute to poor heart health and early death. However, researchers have an incomplete understanding of how differences in the brain functions in people living with SMIs impact the connection between their social perception and self-reported, lived experience of social withdrawal, isolation, or loneliness.

How did the researchers conduct the study?

Researchers from Boston University and Harvard Medical School conducted a selective narrative review of studies addressing social withdrawal, isolation, loneliness, and health in SMI.

Their review highlighted evidence indicating differences in brain activity between people experiencing loneliness and those who are not, particularly in regions associated with social cognitive processes. Additionally, neuroimaging studies have shown increased activation in brain areas responsible for risk assessment among lonely individuals.

Furthermore, the researchers discussed findings suggesting that individuals experiencing loneliness, who perceive others negatively and exhibit signs of psychopathology, may misinterpret social cues, leading to social disconnection. Over time, this social disconnection can prompt a defensive response to social situations, further reducing motivation for social interaction.

What did the study results show?

Based on a synthesis of recent findings that indicate a causal relationship between loneliness and nervous system responses in the human body that cause inflammation and reduce immunity, the authors developed a testable model of the psychological and neural mechanisms of social disconnection in SMI. They hypothesize that people living with SMI are more likely to experience high levels of chronic psychological stress and therefore, more likely to experience persistently high levels of physiological inflammation. Stress and inflammation biomarkers can serve as indicators of an unmet need for social connection. Health providers and caregivers could use these indicators to provide social support and connection to those experiencing this need.

What is the potential impact of these findings?

The authors suggest that once their hypothesis has been rigorously tested and verified, new methods to improve health outcomes for people living with SMI may be developed, including potential “just-in-time” digital interventions through mobile devices. The authors also suggest that people living with SMI and experiencing loneliness can receive interventions that address any potential negative beliefs they hold about rejection, thus interrupting the cycle of social isolation.

Citation: Fulford D, Holt DJ. Social Withdrawal, Loneliness, and Health in Schizophrenia: Psychological and Neural Mechanisms . Schizophr Bull. 2023 Sep 7;49(5):1138-1149. doi: 10.1093/schbul/sbad099. PMID: 37419082; PMCID: PMC10483452.

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Mechanism Reform: An Application to Child Welfare

In many market-design applications, a new mechanism is introduced to reform an existing institution. Compared to the design of a mechanism in isolation, the presence of a status-quo system introduces both challenges and opportunities for the designer. We study this problem in the context of reforming the mechanism used to assign Child Protective Services (CPS) investigators to reported cases of child maltreatment in the U.S. CPS investigators make the consequential decision of whether to place a child in foster care when their safety at home is in question. We develop a design framework built on two sets of results: (i) an identification strategy that leverages the status-quo random assignment of investigators—along with administrative data on previous assignments and outcomes—to estimate investigator performance; and (ii) mechanism-design results allowing us to elicit investigators’ preferences and efficiently allocate cases. This alternative mechanism can be implemented by setting personalized non-linear rates at which each investigator can exchange various types of cases. In a policy simulation, we show that this mechanism reduces the number of investigators’ false positives (children placed in foster care who would have been safe in their homes) by 10% while also decreasing false negatives (children left at home who are subsequently maltreated) and overall foster care placements. Importantly, the mechanism is designed so that no investigator is made worse-off relative to the status quo. We show that a naive approach which ignores investigator preference heterogeneity would generate substantial welfare losses for investigators, with potential adverse effects on investigator recruitment and turnover.

We thank Peter Arcidiacono, Pat Bayer, Sylvain Chassang, Michael Dinerstein, Laura Doval, Joseph Doyle, Federico Echenique, Natalia Emanuel, Maria Fitzpatrick, Ezra Goldstein, Peter Hull, Chris Mills, Matt Pecenco, Katherine Rittenhouse, Alex Teytelboym and seminar participants at the 2024 ASSA Annual Meeting, SAET 2024, UC Berkeley, and Duke University for helpful comments and suggestions. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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    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 ...

  4. How to Write the Discussion Section of a Research Paper

    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 ...

  5. How To Write A Dissertation Discussion Chapter

    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

  6. PDF Discussion Section for Research Papers

    The discussion section is one of the final parts of a research paper, in which an author describes, analyzes, and interprets their findings. They explain the significance of those results and tie everything back to the research question(s). In this handout, you will find a description of what a discussion section does, explanations of how to ...

  7. How Do I Write the Discussion Chapter?

    The Discussion chapter brings an opportunity to write an academic argument that contains a detailed critical evaluation and analysis of your research findings. This chapter addresses the purpose and critical nature of the discussion, contains a guide to selecting key results to discuss, and details how best to structure the discussion with ...

  8. (PDF) How to Write an Effective Discussion

    The discussion section, a systematic critical appraisal of results, is a key part of a research paper, wherein the authors define, critically examine, describe and interpret their findings ...

  9. Organizing Academic Research Papers: 8. The 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 ...

  10. Discussion and Conclusion

    The discussion section is the heart of any scientific paper. This is where new thoughts and directions are introduced and where reviewers provide context and meaning to their research findings (Hess 2004).Some suggest that the discussion is the most difficult part of a literature review to write (Aveyard 2019) and that it demands the most effort and critical thinking of reviewers (Kearney 2017).

  11. How to Write an Effective Discussion in a Research Paper; a Guide to

    Discussion is mainly the section in a research paper that makes the readers understand the exact meaning of the results achieved in a study by exploring the significant points of the research, its ...

  12. Discussing your findings

    Your discussion should begin with a cogent, one-paragraph summary of the study's key findings, but then go beyond that to put the findings into context, says Stephen Hinshaw, PhD, chair of the psychology department at the University of California, Berkeley. "The point of a discussion, in my view, is to transcend 'just the facts,' and engage in ...

  13. Communicating and disseminating research findings to study participants

    The researcher interview guide was designed to understand researchers' perspectives on communicating and disseminating research findings to participants; explore past experiences, if any, of researchers with communication and dissemination of research findings to study participants; document any approaches researchers may have used or intend ...

  14. Research Findings

    Qualitative Findings. Qualitative research is an exploratory research method used to understand the complexities of human behavior and experiences. Qualitative findings are non-numerical and descriptive data that describe the meaning and interpretation of the data collected. Examples of qualitative findings include quotes from participants ...

  15. Dissertations 5: Findings, Analysis and Discussion: Home

    if you write a scientific dissertation, or anyway using quantitative methods, you will have some objective results that you will present in the Results chapter. You will then interpret the results in the Discussion chapter. B) More common for qualitative methods. - Analysis chapter. This can have more descriptive/thematic subheadings.

  16. Research Summary

    Research Summary. Definition: A research summary is a brief and concise overview of a research project or study that highlights its key findings, main points, and conclusions. It typically includes a description of the research problem, the research methods used, the results obtained, and the implications or significance of the findings.

  17. Writing the Discussion Section: Describing the Significance of the

    The authors further state that if the findings and results of a research study are portrayed in a clear and concise manner, it becomes the foundation for future research studies (Hahn Fox ...

  18. How to Write an "Implications of Research" Section

    To summarize, remember these key pointers: Implications are the impact of your findings on the field of study. They serve as a reflection of the research you've conducted. They show the specific contributions of your findings and why the audience should care. They can be practical or theoretical. They aren't the same as recommendations.

  19. Looking forward: Making better use of research findings

    The study of the diffusion of innovations—how new ideas are transmitted through social networks—has been influential in illustrating that those who adopt new ideas early tend to differ in a number of ways from those who adopt the ideas later. ... Some examples of barriers to the application of research findings to patients are given in the ...

  20. Implications in Research

    Implications in Research. Implications in research refer to the potential consequences, applications, or outcomes of the findings and conclusions of a research study. These can include both theoretical and practical implications that extend beyond the immediate scope of the study and may impact various stakeholders, such as policymakers ...

  21. How to Write a Discussion Section

    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.

  22. PDF Chapter 4 Key Findings and Discussion

    -New directions of Smart Clothing application-Smart Clothing's context Chapter 4 Key Findings and Discussion This chapter presents principal findings from the primary research. The findings can be divided into two groups: qualitative and quantitative results. Figure 4.1 illustrates how these two types of results are integrated.

  23. Intranasal neomycin evokes broad-spectrum antiviral immunity in the

    In this study, we assessed whether intranasal application of neomycin, an aminoglycoside antibiotic, evokes ISG-based antiviral protection against SARS-CoV-2 and influenza A virus infection in the upper respiratory tract in mice, and whether nasal neomycin application confers transmission blockade in hamsters.

  24. Research ethics and artificial intelligence for global health

    The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [1,2,3].Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health ...

  25. Neurophysiological insights into sequential decision-making: exploring

    In conclusion, our study contributes to the broader understanding of decision-making research, linking neurophysiological data with theoretical and practical applications. Our study aims to contribute to bridging the gap between academic research and real-world applications, hoping that our findings will inspire further exploration and the ...

  26. A qualitative simulation checking approach of programmed ...

    The validity and reliability of a grounded theory research based on interpretivism involves four dimensions: credibility, transferability, dependability, and confirmability. In order to enhance the credibility of a qualitative study, the findings of the grounded theory need to be checked for consistency with reality. Traditional checking approaches lack universal applicability and are ...

  27. A New Model for Studying Social Isolation and Health in People with

    Researchers have developed a promising new framework for studying the link between social disconnection and poor physical health in people living with serious mental illnesses (SMI). Drawing on published research from animal models and data from the general population, this framework builds on existing social isolation and loneliness models by integrating insights from evolutionary and ...

  28. Conceptually Funded Usability Evaluation of an Application for ...

    The focus of this study, and the subject of this article, resides in the conceptually funded usability evaluation of an application of descriptive models to a specific dataset obtained from the East Slovak Institute of Heart and Vascular Diseases targeting cardiovascular patients. Delving into the current state-of-the-art practices, we examine the extent of cardiovascular diseases, descriptive ...

  29. Mechanism Reform: An Application to Child Welfare

    In many market-design applications, a new mechanism is introduced to reform an existing institution. Compared to the design of a mechanism in isolation, the presence of a status-quo system introduces both challenges and opportunities for the designer. We study this problem in the context of ...

  30. CHAPTER FIVE DISCUSSION OF THE FINDINGS, CONCLUSIONS AND ...

    5.0: Introduction. This chapter aims to summarize the outcome of th e results and findings of presentations from the. survey. Its attempt to give general discussion as well as linking the findi ...