Anti-harassment policies across Canadian and international medical programs: strengths, areas for improvement, and a need for standardization
Bibliographic record
Abstract
Background/Purpose: Medical trainee harassment is a global issue that has led to a multitude of detrimental effects. An important area of consideration is whether harassment policies are clear and available to all medical trainees globally. We aimed to develop a standardized rubric for evaluating anti-harassment policies and assess policies across Canadian medical schools and top international universities to identify strengths and areas for improvement. Methods: We constructed a rubric by synthesizing criteria from established frameworks on harassment policy effectiveness, adapting key elements to assess clarity, accessibility, and comprehensiveness in medical school policies. On March 2023, we evaluated 58 harassment policies from 16 Canadian medical schools and 31 policies from eight of the top 10 Quacquarelli Symonds (QS)-ranked universities. Our rubric, developed from four key frameworks, scored policies across three themes: (1) Policy Foundation, (2) Complaint Procedures, and (3) Resolution and Implementation. Results: Canadian universities performed well in foundational policy areas (average score 83.00% on Theme 1) but showed meaningful gaps in Complaint Procedures (48.75%) and Resolution and Implementation (39.38%). Top international QS-ranked universities similarly scored low in these latter themes, though they performed better on formal complaint processes. Key areas needing improvement include informal complaint procedures and timelines for response in Canadian universities, and policy revision commitments in top QS-ranked universities. Conclusions: This study highlights the need for enhanced anti-harassment policies, particularly in complaint and resolution procedures. Our rubric provides a structured approach for policy evaluation, enabling Canadian and potentially international institutions to improve policy clarity, accessibility, and comprehensiveness, fostering safer training environments.
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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 | 0.201 | 0.265 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.022 | 0.028 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".