Competence committees decision-making; an interplay of data, group orientation, and intangible impressions
Bibliographic record
Abstract
BACKGROUND: The implementation of competency-based medical education and utilization of competence committees (CC) represents a paradigm shift in residency education. This qualitative study aimed to explore the methods used by two operational CC and their members to make decisions about progression and competence of their residents. METHODS: An instrumental case study methodology was used to study the CC of two postgraduate training programs. Transcripts from observed CC meetings, member interviews, and guiding documents were analyzed using a constructivist grounded theory approach to reveal themes explaining the decision-making process. RESULTS: Our study found that the CC followed a process that began within a social decision schema model and evolved to a discussion that invoked social influence theory, shared mental models, and social judgment scheme to clarify the points of contention. We identified that the CC decision-making was at risk of bias, primarily influenced by the group composition, the group orientation and individual members' mindset, as well as their personal experiences with the trainees. CONCLUSIONS: Increased awareness of the sources of bias in CC functioning and familiarity with the CC role in competency-based medical education would enable committees to provide valuable feedback to all trainees regardless of their trajectory.
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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 | 0.001 | 0.012 |
| 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) | 0.001 | 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".