Structural Violence Education: A Critical Moment for Psychiatric Training
Bibliographic record
Abstract
The mental health ramifications of structural violence are borne disproportionately by marginalized patient populations in North America, which includes Black, Indigenous, and 2SLGBTQIA+ communities and people who use drugs. Structural violence can comprise, for example, police or state violence, colonialism, and medical violence. We chronicle the history of psychiatric discourse around structural violence over the past 50 years and highlight the critical need for new formalized competencies to become incorporated into the training of medical students across Canada, specifically addressing the impacts of structural violence for the aforementioned populations. Finally, we offer a framework of learning objectives for designing educational sessions discussing structural violence and mental health for integration into pre-clerkship psychiatry curricula at medical schools across Canada.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.014 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.024 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".