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Record W4391691181 · doi:10.1111/tct.13746

Valuable groundwork: Using your Master's research project to contribute to a scholarly conversation

2024· article· en· W4391691181 on OpenAlexaff
Lorelei Lingard

Bibliographic record

VenueThe Clinical Teacher · 2024
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsCourseworkContext (archaeology)Medical educationConversationMentorshipPolitical scienceLibrary sciencePublic relationsPsychologySociologyMedicineHistoryComputer science

Abstract

fetched live from OpenAlex

In the past 30 years, Master-level programmes in health professions education (MHPE) have grown exponentially, from 7 to 150 worldwide.1, 2 Many of these programmes share a common requirement to conduct original research; some also require its publication in a peer-reviewed journal. So where are all the publications from Master's research project? Doing some quick, conservative math: if each of those 150 MHPE programmes graduates 10 students a year, and half of those have opted for a research rather than coursework-only stream, that is 750 original MHPE research projects annually. HPE scholarly conversations should be full of knowledge from these research projects. But they are not. The problem is not unique to HPE, and studies of thesis publication are relevant and revealing (particularly in the absence of empirical data in the MHPE context). A Croatian study reported that only 14% of Master's theses by medical doctors were published3; a French study found a 17% publication rate of medical trainee theses4; a study of publication output from Master's and doctoral theses in family medicine reported a 21.6% publication rate; a Finnish study of medical diploma theses reported a 23.8% publication rate5; and an assessment of publication rates of mandatory research projects from 10 cohorts of Dutch medical students reported a 27.7% publication rate. Clearly, the vast majority of thesis-style research projects are not contributing to scholarly knowledge. Why not? Those of us who supervise Master's projects know firsthand that many are not submitted for publication, and few of those that are will be accepted. Does that mean that Master's level research projects serve no purpose outside the graduate programme requirement? No. This ‘How to …’ paper offers three strategies to capitalise on the solid groundwork of your Master's research project and contribute to both HPE's knowledge and your own continued scholarly development. Your Master's project was a learning exercise: you learned how to think like a researcher by conducting a small piece of scholarly work. And if you designed it well to serve the Master's purpose, then it will be, by definition, limited. It will have a small sample, or an imperfect design, or a preliminary analysis—it will be a very partial view of whatever complex problem you were nibbling away at. Therefore, it is not going to be able to convincingly answer a research question. But it may be able to provide meaningful insights about how to ask a research question more precisely. For instance, a recent study of Irish obstetrics trainees' lived experiences of breaking bad news framed its aim as exploratory, and its conclusions ‘raised questions’ rather than offering answers.6 Understanding this distinction between a paper that answers and a paper that asks can help you choose an appropriate article type. Instead of submitting your work in the journal submission category of Original Research (and receiving robust criticisms of the many limitations given its Masters scope), consider writing up the story of your work as an Insight article,7 a ‘Really Good Stuff’ or ‘When I Say …’ piece8 or a Scholarly Perspective9 piece. Keep an eye on such sections in the journals you read for nice examples you can use as a model.10, 11 Figure 1 offers a hypothetical example. Arguably, most Master's (and other small-scale) research projects never get published because they never get submitted in the first place. Find a venue that seems to fit, and submit your work for peer review. Peer review feedback will point out your study's strengths and weaknesses, and they may be different than you anticipated. For instance, you may be concerned about your small sample size, but peer reviewers struggled more with your choice of theoretical framing. Even if you are desk-rejected (that is, the Editor decides not to send your work out for peer review), you can still receive valuable insights. We recently submitted a piece of work done by a medical student as a summer project. It was a small-scale work, leveraging an existing data set, but with a unique theoretical angle to the analysis that seemed worthy of its own story. We were desk-rejected—which stings no matter how experienced you are! But the editor provided helpful comments and questions. Some of them we cannot address: for instance, the student does not have the capacity to update the data set. But some, such as clarifying how we used theory in the analytical process, we can use to strengthen the piece for resubmission. At the Master's level, you were narrowly focused on a single project. And rightly so—you needed to get it done! Now that it is completed, though, it also holds value as a small first step in a larger programme of scholarly work. Even if you have not published from the project, try to articulate what you have learned. Did you realise that the way you conceptualised the problem was insufficient? Did you gain insight into how to recruit a busy sample of practitioners? Did you get experience in how to work across disciplinary boundaries? Did you struggle to get good interview data and, in the process, develop new skills in asking questions that get beneath participants' cover story? Any and all such lessons can be used to strengthen the next study you embark on. Use these lessons strategically and explicitly as groundwork for your next proposal or grant. Your small Master's project is like pilot data: it provides rationale and justification for a variety of methodological, ethical and theoretical issues. Can you feel the shift in value? Rather than apologising for a completed project that was narrow in scope, you are now in possession of pilot data from valuable (and perhaps novel) groundwork. Pilot data is often descriptive and does not require the same strength of evidence. Figure 2 offers concrete examples. As the statistics illustrate, publishing from your small research project could be a challenge. But it is not impossible, and it should not be your only goal. If you do hope to publish, you may need to make a critical shift in the expected ‘story’ of an empirical manuscript. Think twice about setting up a question that needs to be answered—you may not be able to satisfy that expectation. Instead, set up a question that needs to be asked, refined, clarified and reconsidered: that you can begin to do with your small project. Submit your work for peer review: do not assume publication is impossible before you try. You will receive valuable feedback that can be leveraged for resubmission. And even if publication is out of reach this time, your Master's project can inform the next proposal or grant you write. As pilot data, that small project becomes evidence of your investment and support for your design decisions, strengthening the case for your unfolding research programme. Lorelei Lingard: Conceptualization; writing—original draft. The authors have no acknowledgement to disclose. The authors have no conflict of interest to disclose.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.041
metaresearch head score (Gemma)0.130
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.130
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.823
GPT teacher head0.653
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
Has abstractyes

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