Should Wellness Be a Core Competency for Physicians?
Bibliographic record
Abstract
There has been a recent rise in calls for action around wellness and physician health. In medical education, wellness has been proposed as a physician competency. In this article, the authors review the history of the "wellness as a competency" concept within U.S. and Canadian residency programs and medical schools. Drawing from literature on the discourses of wellness and competence in medical education, they argue that operationalizing wellness as a physician competency holds profound implications for curricula, admissions, evaluation, and licensure. While many definitions of "wellness" and "competency" are used within medical training environments, the authors argue that the definitions institutions ultimately use will have significant impacts for trainees who are considered "unwell." In particular, medical learners with disabilities-including those with mental health, chronic health, learning, sensory, and mobility disabilities-may not conform to dominant conceptions of "wellness," and there is a risk they will become further stigmatized or even be considered unsuitable to practice in the profession. The authors conclude that framing wellness as a competency has the potential to legitimize support-seeking and prioritize physician health, yet it may also have the potential unintended effect of excluding certain learners from the profession. They propose a universal design approach to understand wellness at a systems level and to remove barriers to wellness for all medical learners.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".