The CanMEDS initiative: implementing an outcomes-based framework of physician competencies
Bibliographic record
Abstract
BACKGROUND: Outcomes-based education in the health professions has emerged as a priority for curriculum planners striving to align with societal needs. However, many struggle with effective methods of implementing such an approach. In this narrative, we describe the lessons learned from the implementation of a national, needs-based, outcome-oriented, competency framework called the CanMEDS initiative of The Royal College of Physicians and Surgeons of Canada. METHODS: We developed a framework of physician competencies organized around seven physician "Roles": Medical Expert, Communicator, Collaborator, Manager, Health Advocate, Scholar, and Professional. A systematic implementation plan involved: the development of standards for curriculum and assessment, faculty development, educational research and resources, and outreach. LESSONS LEARNED: Implementing this competency framework has resulted in successes, challenges, resistance to change, and a list of essential ingredients for outcomes-based medical education. CONCLUSIONS: A multifaceted implementation strategy has enabled this large-scale curriculum change for outcomes-based education.
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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 | 0.097 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".