Home Field Advantage? Comparing the Quality of EPA Observations Completed On- vs Off-Service
Bibliographic record
Abstract
ABSTRACT Background Increasing evidence suggests that supervisors exhibit different assessment behaviors for residents from their own discipline compared to those completing a rotation from another discipline. As programs of assessment rely on collecting robust performance data to inform high-stakes decisions about progress and promotion, it is important to examine the quality of such inputs. Objective To compare the quality of workplace-based assessments (WBAs) of emergency medicine (EM) residents by EM and non-EM assessors. Methods This retrospective database study compared the quality of WBAs using the Quality of Assessment of Learning (QuAL) score (range 0-5), a previously published measure of WBA quality that has demonstrated strong psychometric characteristics. Five entrustable professional activities (EPAs), 3 procedural and 2 non-procedural, mapped to both EM and non-EM rotations, were selected for inclusion. Two hundred and fifty WBAs (50 WBAs per EPA; 25 EM and 25 non-EM), completed from July 2019 to June 2021, were rated by 3 blinded EM physician raters. QuAL scores were analysed using factorial ANOVA. Results Mean QuAL scores for WBAs completed during EM rotations were significantly higher compared to those completed during non-EM rotations (3.66±0.99 vs 3.02±0.99). Further, mean QuAL score for procedural EPAs was significantly higher than non-procedural EPAs (3.61±1.00 vs 3.16±1.03). Conclusions In this study, the quality of WBAs completed for EM residents during non-EM rotations was of lower quality compared to assessments on EM rotations.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".