Competency-Based Medical Education at Scale: A Road Map for Transforming National Systems of Postgraduate Medical Education
Bibliographic record
Abstract
In the past decade, the Canadian system of postgraduate medical education has been transformed with the implementation of a new approach to competency based medical education called Competence by Design. The Royal College of Physicians and Surgeons of Canada (Royal College) developed an approach to time-variable competency based medical education and adapted that design for medical, surgical, and diagnostic disciplines. New educational standards and entrustable professional activities consistent with this approach were co-created with 67 specialties and subspecialties, and implementation was scaled up across 17 universities and over 1000 postgraduate training programs. Partner engagement, systematic design of workshops to create discipline specific competency-based standards of education, and agile adaptation were all key ingredients for success. This paper describes the strategies applied by the Royal College, lessons learned regarding transformative change in the complex system of postgraduate medical education, and the current status of the Competence by Design initiative. The approach taken and lessons learned by the Royal College may be useful for other educators who are planning a transformation to CBME or any other major educational reform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.059 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.020 | 0.021 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".