Competence committees: The steep climb from concept to implementation
Bibliographic record
Abstract
INTRODUCTION: Competence committees (CCs) are groups of educators tasked with reviewing resident progress throughout their training, making decisions regarding the achievement of Entrustable Professional Activities and recommendations regarding promotion and remediation. CCs have been mandated as part of competency-based medical education programmes worldwide; however, there has yet to be a thorough examination of the implementation challenges they face and how this impacts their functioning and decision-making processes. This study examined CC implementation at a Canadian institution, documenting the shared and unique challenges that CCs faced and overcame over a 3-year period. METHODS: This study consisted of three phases, which were conceptually and analytically linked using Moran-Ellis and colleagues' notion of 'following a thread.' Phase 1 examined the early perceptions and experiences of 30 key informants using a survey and semi-structured interviews. Phase 2 provided insight into CCs' operations through a survey sent to 35 CC chairs 1-year post-implementation. Phase 3 invited 20 CC members to participate in semi-structured interviews to follow up on initial themes 2 years post-implementation. Detailed observation notes from 16 CC meetings across nine disciplines were used to corroborate the findings from each phase. RESULTS: Response rates in each phase were 83% (n = 25), 43% (n = 15) and 60% (n = 12), respectively. Despite the high degree of support for CCs among faculty and resident members, several ongoing challenges were highlighted: adapting to programme size, optimising membership, engaging residents, maintaining capacity among members, sharing and aggregating data and developing a clear mandate. DISCUSSION: Findings of this study reinforce the importance of resident engagement and information sharing between disciplines. Challenges faced by CCs are discussed in relation to the existing literature to inform a better understanding of group decision-making processes in medical education. Future research could compare implementation practices across sites and explore which adaptations lead to better or worse decision-making outcomes.
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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 | 0.000 | 0.004 |
| 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) | 0.011 | 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".