Implementation of competence committees during the transition to CBME in Canada: A national fidelity-focused evaluation
Bibliographic record
Abstract
PURPOSE: This study evaluated the fidelity of competence committee (CC) implementation in Canadian postgraduate specialist training programs during the transition to competency-based medical education (CBME). METHODS: A national survey of CC chairs was distributed to all CBME training programs in November 2019. Survey questions were derived from guiding documents published by the Royal College of Physicians and Surgeons of Canada reflecting intended processes and design. RESULTS: Response rate was 39% (113/293) with representation from all eligible disciplines. Committee size ranged from 3 to 20 members, 42% of programs included external members, and 20% included a resident representative. Most programs (72%) reported that a primary review and synthesis of resident assessment data occurs prior to the meeting, with some data reviewed collectively during meetings. When determining entrustable professional activity (EPA) achievement, most programs followed the national specialty guidelines closely with some exceptions (53%). Documented concerns about professionalism, EPA narrative comments, and EPA entrustment scores were most highly weighted when determining resident progress decisions. CONCLUSIONS: Heterogeneity in CC implementation likely reflects local adaptations, but may also explain some of the variable challenges faced by programs during the transition to CBME. Our results offer educational leaders important fidelity data that can help inform the larger evaluation and transformation of CBME.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.041 | 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 teacher head, 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".