Ideas to Action: Using Curriculum Design to Develop a “Roadmap to Wellness” Curriculum
Bibliographic record
Abstract
Introduction: Physician burnout, well-being, and professional fulfillment are deeply intertwined topics that are increasingly recognized as affecting the lives of physicians, health care workers, and patients alike. The Accreditation Council for Graduate Medical Education (ACGME) mandates that all residencies address wellness within the context of residency training without providing much guidance on how to do so. Emergency Medicine organizations such as the American College of Emergency Physicians, the American Academy of Emergency Physicians, the Society for Academic Emergency Medicine, and the Council of Residency Directors of Emergency Medicine (CORD) suggest that one method to address wellness is in the form of a curriculum. Successfully developing or modifying a curriculum to work for individual residency programs can be a difficult task. Methods: The CORD Resilience Committee Wellness Curriculum Subcommittee comprised of experts in physician wellness and medical education started by conducted literature searches on terms related to burnout and wellness and searching the internet for documented wellness curricula, models and resources. Using this information and a standard curriculum development process, they created a roadmap for developing (or modifying), initiating, and evaluating a wellness curriculum. Conclusion: Wellness curricula are not a one-size-fits-all situation. Using the checklist and guidelines in this white paper, readers can individualize existing wellness curricula to help foster physician well-being.
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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 | 0.054 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".