A Signal Through the Noise: Do Professionalism Concerns Impact the Decision Making of Competence Committees?
Bibliographic record
Abstract
PURPOSE: To characterize how professionalism concerns influence individual reviewers' decisions about resident progression using simulated competence committee (CC) reviews. METHOD: In April 2017, the authors conducted a survey of 25 Royal College of Physicians and Surgeons of Canada emergency medicine residency program directors and senior faculty who were likely to function as members of a CC (or equivalent) at their institution. Participants took a survey with 12 resident portfolios, each containing hypothetical formative and summative assessments. Six portfolios represented residents progressing as expected (PAE) and 6 represented residents not progressing as expected (NPAE). A professionalism variable (PV) was developed for each portfolio. Two counterbalanced surveys were developed in which 6 portfolios contained a PV and 6 portfolios did not (for each PV condition, 3 portfolios represented residents PAE and 3 represented residents NPAE). Participants were asked to make progression decisions based on each portfolio. RESULTS: Without PVs, the consistency of participants giving scores of 1 or 2 (i.e., little or no need for educational intervention) to residents PAE and to those NPAE was 92% and 10%, respectively. When a PV was added, the consistency decreased by 34% for residents PAE and increased by 4% for those NPAE (P = .01). CONCLUSIONS: When reviewing a simulated resident portfolio, individual reviewer scores for residents PAE were responsive to the addition of professionalism concerns. Considering this, educators using a CC should have a system to report, collect, and document professionalism issues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.094 | 0.486 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".