Medical student wellness in Canada: time for a national curriculum framework
Bibliographic record
Abstract
There is substantial evidence showing that medical student wellness is a worsening problem in Canada. It is apparent that medical students' wellness deteriorates throughout their training. Medical schools and their governing bodies are responding by integrating wellness into competency frameworks and accreditation standards through a combination of system- and individual-level approaches. System-level strategies that consider how policies, medical culture, and the "hidden curriculum" impact student wellness, are essential for reducing burnout prevalence and achieving optimal wellness outcomes. Individual-level initiatives such as wellness programming are widespread and more commonly used. These are often didactic, placing the onus on the student without addressing the learning environment. Despite significant progress, there is little programming consistency across schools or training levels. There is no wellness curriculum framework for Canadian undergraduate medical education that aligns with residency competencies. Creating such a framework would help align individual- and system-level initiatives and smooth the transition from medical school to residency. The framework would organize goals within relevant wellness domains, allow for local adaptability, consider basic learner needs, and be learner-informed. Physicians whose wellness has been supported throughout their training will positively contribute to the quality of patient care, work environments, and in sustaining a healthy Canadian population.
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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 | 0.031 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".