How to … Study Sensitive Topics
Bibliographic record
Abstract
Health professions education inevitably exposes learners to socially, emotionally, and ethically sensitive experiences-ranging from academic struggle and mistreatment to emotionally charged clinical encounters. These moments, often occurring in hierarchical and high-pressure clinical settings, can profoundly shape learners' professional identities and well-being. As educators strive to create more humane learning environments, it becomes essential to critically examine how such sensitive topics are navigated, studied, and represented in research. This paper outlines a framework for conducting research on sensitive topics in health professions education. Drawing on examples from our own programmes of research, we explore methodological, ethical, and practical considerations across six stages of the research process: selecting a topic, study design, reflexivity, data collection, analysis, and dissemination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.131 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".