How are psychosocial risks taught to Canadian occupational medicine residents?
Bibliographic record
Abstract
BACKGROUND: Current Canadian Occupational Medicine residency program training routes and curriculum are being redesigned, using a competence by design model and reverting to a primary entry specialty likely in 2027. This is an opportunity to improve training standards and better prepare residents to address psychosocial risk prevention and worker's compensation. AIMS: To conduct an environmental scan on the educational experience pertaining to mental health and psychosocial risks. METHODS: We conducted an environmental scan in 2025 collecting data on requirements, educational experiences, and examination on mental health and psychosocial risks for occupational medicine residents via contacts with program directors and the chair of the subspecialty exam committee, review of the national academic half-day curriculum, Royal College of Physicians and Surgeons of Canada standards and objectives, accreditation standards, and a recommended reference textbook. RESULTS: Few requirements are mandated in specialty accreditation standards, mostly about mental health and addiction medicine experiences, delivered by all programs. The national academic half-day curriculum includes 8% of programmed sessions on these issues (mental health and addiction medicine diagnosis, stress models, burnout, bullying, psychosocial risks and work organization). Several additional learning experiences were noted locally such as learning modules or optional webinars. CONCLUSIONS: Programs go beyond requirements and deliver educational experiences addressing psychosocial factors. Canadian academic occupational medicine specialists have been able to influence educational standards at the undergraduate level and have an opportunity to do the same with the updated residency standards. An international framework for psychosocial risks education in occupational medicine residency training may be helpful.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".