Twelve tips for ethical approval for research in health professions education
Bibliographic record
Abstract
BACKGROUND: A growing number of faculty are engaging in research in health professions education. Suggestions continue to be made in the literature for a clear and less onerous pathway for the ethical review of this work. AIM: We aim to provide advice about the ethics application process for those conducting research in health professions education. METHODS: We used critical reflection of our experiences as research ethics board (REB) members, applying for, reviewing and consulting about the ethics application process in both UK and Canadian health contexts in addition to evidence and advice that is available in the literature to inform the tips provided. RESULTS: Twelve tips are offered to help faculty understand and navigate through the ethics application process. CONCLUSION: Health professionals have an important role to play in advancing the field of health professions education, and despite issues identified with current review pathways, REB review is in place to ensure that this work is undertaken safely and ethically. We believe the tips offered in this article will help faculty identify, and devise plans to address, some ethical issues that are common in health professions education research
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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 | 0.499 | 0.607 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.014 | 0.039 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.027 | 0.058 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".