Resident-focused trauma-informed medical education policies: an environmental scan of Canadian medical schools and partner organizations
Bibliographic record
Abstract
Background: Psychological trauma among resident physicians (residents) is common yet underrecognized, even though it can significantly impact learning, patient care, and well-being. Trauma-informed approaches are one way in which trauma can be mitigated. The purpose of this study was to examine institutional policies related to resident-focused trauma-informed medical education (RF-TIME) at Canadian institutions involved in providing and governing physician training. Methods: We conducted an environmental scan of publicly available online content related to RF-TIME at Canadian medical schools (n = 18) and partner organizations (n = 42), initially focusing on policy, and then broadening our scan to include strategic planning, standards, guidelines, reports, educational documents and support resources. Findings were tabulated and synthesized. Results: We were unable to find RF-TIME-specific policies at any Canadian medical school or partner organization. Thirteen schools briefly mentioned RF-TIME approaches within strategic planning (n = 3 schools), policies not focused on trauma (n = 9), guidelines (n = 1), reports (n = 3), educational resources (n = 3), and/or support resources (n = 8). Seventeen partner organizations included RF-TIME content within strategic planning (n = 2 organizations), standards (n = 2), guidelines (n = 1), reports (n = 9), educational resources (n = 2), and/or support resources (n = 4). Conclusions: Resident-focused policies around trauma-informed approaches to medical education are absent within Canadian institutions and organizations involved in the training and regulation of physicians. Developing and implementing RF-TIME policies may help establish more supportive learning environments for medical trainees with psychological post-traumatic injury.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".