Is Competency-Based Medical Education being implemented as intended? Early lessons learned from Physical Medicine and Rehabilitation
Bibliographic record
Abstract
Background: As competency-based medical education (CBME) curricula are introduced in residency programs across Canada, systematic evaluation efforts are needed to ensure fidelity of implementation. This study evaluated early outcomes of CBME implementation in one Canadian Physical Medicine and Rehabilitation program that was an early adopter of CBME, with an aim to inform continuous quality improvement initiatives and CBME implementation nationwide. Methods: Using Rapid Evaluation methodology, informed by the CBME Core Components Framework, the intended outcomes of CBME were compared to actual outcomes. Results: Results suggested that a culture of feedback and coaching already existed in this program prior to CBME implementation, yet faculty felt that CBME added a framework to support feedback. The small program size was valuable in fostering strong relationships and individualized learning. However, participants expressed concerns about CBME fostering a reductionist approach to the development of competence. Challenges existed with direct observation, clear expectations for off-service training experiences, and tracking trainee progress. There was trepidation surrounding national curricular change, yet the institution-wide approach to CBME implementation created shared experiences and a community of practice. Conclusions: Program evaluation can help understand gaps between planned versus enacted implementation of CBME, and foster adaptations to improve the fidelity of implementation.
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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 | 0.025 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".