Understanding Competence Committee Implementation and Decision-Making Practices in the Era of Competency-Based Medical Education
Bibliographic record
Abstract
Competence committees are groups of educators that monitor the progress of medical trainees and decide when they should be promoted to the next stage of training. They represent an important part of modern-day competency-based medical education programs, yet relatively little is known about their implementation and decision-making practices. This thesis seeks to fill a critical gap in the literature by generating empirical evidence with respect to competence committee implementation and decision-making practices across multiple programs. The first data chapter uses a multi-method approach to examine competence committee implementation practices at a Canadian institution over a three-year period. The second and third chapters examine how individuals and groups make promotion decisions, respectively. These chapters also consider the role of non-traditional data sources, such as anecdotal evidence, in competence committees’ decision-making processes. The final data chapter considers the role of social influences and power and examines how factors such as members’ position on the committee, gender, and race/ethnicity influence their contributions to the committee. This thesis provides insight into some of the challenges that exist with respect to competence committee implementation and offers potential solutions based on best practices across multiple programs. It also highlights factors that can influence competence committee decision making and discusses ways that their decision-making processes can be optimized. Broader implications of this thesis, including the role of groups in solving complex problems and the importance of diversity (both in terms of demographics and functional specialization) in ensuring good decision-making outcomes, are also discussed.
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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 | 0.070 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".