Towards an autonomy-supportive model of wellness in Canadian medical education
Bibliographic record
Abstract
Purpose: Learner distress is a huge problem in medicine today, and medical institutions have been called upon to help solve this issue. Unfortunately, the majority have responded not by addressing the system and culture that have long plagued the profession, but by creating individual-focused "wellness" interventions (IFWs). As a result, medical learners are routinely being forced to undergo training on resilience, mindfulness, and burnout. Approach: Grounded in well-supported theory and empirical evidence, my central argument in this commentary is that IFWs are inappropriate, insulting, and psychologically harmful to learners, and that they need to stop. Contribution: Extending prior work in this area, I first present three fundamental problems with IFWs. I then recommend a paradigm shift in how we are approaching "wellness" in medical education. Conclusion: Finally, I provide an evidence-based roadmap, in self-determination theory, for how system-level improvements could be made in a timely, sustainable, and socially responsible way, that would benefit everyone in medicine-from leaders, to educators, to learners, to patients.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".