Implementing Competence Committees on a National Scale: Design and Lessons Learned
Bibliographic record
Abstract
Competence committees (CCs) are a recent innovation to improve assessment decision-making in health professions education. CCs enable a group of trained, dedicated educators to review a portfolio of observations about a learner's progress toward competence and make systematic assessment decisions. CCs are aligned with competency based medical education (CBME) and programmatic assessment. While there is an emerging literature on CCs, little has been published on their system-wide implementation. National-scale implementation of CCs is complex, owing to the culture change that underlies this shift in assessment paradigm and the logistics and skills needed to enable it. We present the Royal College of Physicians and Surgeons of Canada's experience implementing a national CC model, the challenges the Royal College faced, and some strategies to address them. With large scale CC implementation, managing the tension between standardization and flexibility is a fundamental issue that needs to be anticipated and addressed, with careful consideration of individual program needs, resources, and engagement of invested groups. If implementation is to take place in a wide variety of contexts, an approach that uses multiple engagement and communication strategies to allow for local adaptations is needed. Large-scale implementation of CCs, like any transformative initiative, does not occur at a single point but is an evolutionary process requiring both upfront resources and ongoing support. As such, it is important to consider embedding a plan for program evaluation at the outset. We hope these shared lessons will be of value to other educators who are considering a large-scale CBME CC implementation.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".