Fostering a Health-Promoting Learning Environment in Medical Education: Adapting the Okanagan Charter for Administrators and Medical Educators
Bibliographic record
Abstract
Medical students enter medical school with similar or even better well-being than their age-matched peers in other educational programs, but there is predictable erosion of their well-being following matriculation. Interventions to counter this erosion predominantly focus on the individual level; however, significant systemic issues persist that thwart meaningful change. Effectively reforming the learning environment and more broadly targeting problematic aspects of the culture of medical education are essential steps to advance efforts to improve medical learner well-being. Although a healthy environment may allow learners to be well in the educational setting, a health-promoting learning environment strives to promote and embed well-being across all aspects of the learner's experience. Health-promoting learning environments operate by infusing health principles into all aspects of operations, practices, mandates, and businesses. The Okanagan Charter is a widely adopted international framework with principles for best practices of adoption. This charter has the recent endorsement of the Association of Faculties of Medicine of Canada, representing all faculties of medicine in Canada, and serves as a framework for reassessing work on well-being in medical education. In response to this endorsement, the authors have adapted the 5 strategies from the charter for pragmatic integration into the medical education environment and added a sixth strategy: (1) embed health in all policies; (2) develop sustainable, supportive spaces; (3) create thriving medical communities and culture; (4) encourage, support, and sustain meaningful personal development; (5) review, develop, and strengthen faculty-level health services; and (6) collaborate and invest in continuous improvement and evaluation. For each of these 6 strategic directions, actionable steps for implementation in academic medicine are provided to create sustainable and meaningful change.
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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 | 0.035 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".