LO55: Signal & noise – do professionalism concerns impact decision-making of competence committees?
Bibliographic record
Abstract
Introduction: Competence committees (CCs) struggle with incorporating professionalism issues into resident progression decisions. This study examined how professionalism concerns influence individual faculty decisions about resident progression using simulated CC reviews. Methods: In 2017, the investigators conducted a survey of 25 program directors of Royal College emergency medicine residency training programs in Canada and those faculty members who are members of the CCs (or equivalent) at their home institution. The survey contained twelve resident portfolios, each containing formative and summative information available to a CC for making progression decisions. Six portfolios outlined residents progressing as expected and six were not progressing as expected. Further, a professionalism variable (PV) was added to six portfolios, evenly split between those residents progressing as expected and not. Participants were asked to make progression decisions based on each portfolio. Results: Raters were able to consistently identify a resident needing an educational intervention versus those who did not. When a PV was added, the consistency among raters decreased by 34.2% in those residents progressing as expected, versus increasing by 3.8% in those not progressing as expected (p = 0.01). Conclusion: When using an unstructured review of a simulated resident portfolio, individual reviewers can better discriminate between trainees progressing as expected when professionalism concerns are added. Considering this, educators using a competence committee in a CBME program must have a system to acquire and document professionalism issues to make appropriate progress decisions.
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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 | 0.031 | 0.190 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".