The promise, perils, problems and progress of competency‐based medical education
Bibliographic record
Abstract
CONTEXT: Competency-based medical education (CBME) is being adopted wholeheartedly by organisations worldwide in the hope of meeting today's expectations for training a competent doctor. But are we, as medical educators, fulfilling this promise? METHODS: The authors explore, through a personal viewpoint, the problems identified with CBME and the progress made through the development of milestones and entrustable professional activities (EPAs). RESULTS: Proponents of CBME have strong reasons to keep developing and supporting this broad movement in medical education. Critics, however, have legitimate reservations. The authors observe that the recent increase in use of milestones and EPAs can strengthen the purpose of CBME and counter some of the concerns voiced, if properly implemented. CONCLUSIONS: The authors conclude with suggestions for the future and how using EPAs could lead us one step closer to the goals of not only competency-based medical education but also competency-based medical practice.
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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 | 0.111 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.019 |
| 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".