Addressing medical resident mistreatment: A resident-centred approach
Bibliographic record
Abstract
Mistreatment negatively impacts the wellbeing of medical learners and is related to worse patient outcomes and team functioning. Resident perspectives on improving mistreatment reporting structures and investigations have not been explored. We aimed to understand residents’ views on safe reporting structures, investigations, and resolution processes. We conducted an exploratory sequential mixed method study beginning with a series of qualitative interviews to inform an anonymous online survey to all Dalhousie University residents (N = 645). When interviewed, residents (N = 10) discussed personal experiences with mistreatment, barriers to reporting, and how these processes could better serve them. Themes from the interviews were imbedded in an anonymous online survey to explore their prevalence among a larger group. Residents (N = 120; 19%) completed the online survey and revealed that mistreatment was very common yet underreported. Barriers to reporting included confidentiality concerns, perceptions that reporting would not change anything, and fear of retaliation. Desired outcomes for perpetrators depended on the perpetrator’s position and incident severity, and most prefer a remedial approach. Resident mistreatment remains prevalent and current processes of dealing with reports may be inadequate. Residents have thoughtful insights for improving institutional policies and procedures and should be meaningfully engaged.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".