It’s a Marathon, Not a Sprint: Rapid Evaluation of Competency-Based Medical Education Program Implementation
Bibliographic record
Abstract
PURPOSE: Despite the broad endorsement of competency-based medical education (CBME), myriad difficulties have arisen in program implementation. The authors sought to evaluate the fidelity of implementation and identify early outcomes of CBME implementation using Rapid Evaluation to facilitate transformative change. METHOD: Case-study methodology was used to explore the lived experience of implementing CBME in the emergency medicine postgraduate program at Queen's University, Canada, using iterative cycles of Rapid Evaluation in 2017-2018. After the intended implementation was explicitly described, stakeholder focus groups and interviews were conducted at 3 and 9 months post-implementation to evaluate the fidelity of implementation and early outcomes. Analyses were abductive, using the CBME core components framework and data-driven approaches to understand stakeholders' experiences. RESULTS: In comparing planned with enacted implementation, important themes emerged with resultant opportunities for adaption. For example, lack of a shared mental model resulted in frontline difficulty with assessment and feedback and a concern that the granularity of competency-focused assessment may result in "missing the forest for the trees," prompting the return of global assessment. Resident engagement in personal learning plans was not uniformly adopted, and learning experiences tailored to residents' needs were slow to follow. CONCLUSIONS: Rapid Evaluation provided critical insights into the successes and challenges of operationalizing CBME. Implementing the practical components of CBME was perceived as a sprint, while realizing the principles of CBME and changing culture in postgraduate training was a marathon requiring sustained effort in the form of frequent evaluation and continuous faculty and resident development.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".