Seven ways to get a grip on implementing Competency-Based Medical Education at the program level
Bibliographic record
Abstract
Competency-based medical education (CBME) curricula are becoming increasingly common in graduate medical education. Put simply, CBME is focused on educational outcomes, is independent of methods and time, and is composed of achievable competencies.1 In spite of widespread uptake, there remains much to learn about implementing CBME at the program level. Leveraging the collective experience of program leaders at Queen’s University, where CBME simultaneously launched across 29 specialty programs in 2017, this paper leverages change management theory to provide a short summary of how program leaders can navigate the successful preparation, launch, and initial implementation of CBME within their residency programs.
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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 | 0.081 | 0.091 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.015 | 0.032 |
| Scholarly communication | 0.031 | 0.043 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.016 | 0.039 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".