Family Medicine Clerkship Directors’ Perspectives on USMLE Pass/Fail Scoring: A CERA Study
Bibliographic record
Abstract
INTRODUCTION: Reports on the effects of changing the United States Medical Licensing Exam (USMLE) Step 1 examination scoring to pass/fail are evolving in the medical literature. This Council of Academic Family Medicine Educational Research Alliance family medicine clerkship directors' study seeks to describe family medicine clerkship directors' perceptions on the impact of incorporation of Step 1 pass/fail score reporting on students' family medicine clerkship performance. METHODS: Ninety-six clerkship directors responded (56.8% response rate). After exclusion of Canadian schools, we analyzed 88 clerkship directors' responses from US schools. We used descriptive statistics for demographics and responses to survey questions. We used ꭓ2 analysis to determine statistically significant associations between survey items. RESULTS: Clerkship directors did not observe changes in students' overall clinical performance after Step 1 pass/fail scoring (60.8%). Fifty percent of clerkship directors reported changes in Step 1 timing recommendations in the past 3 years. Reasons included curriculum redesign (30.5%), COVID (4.5%), change in Step 1 to pass/fail (11.0%), and other reasons (3.7%). Forty-five percent of these clerkship directors did not observe a change in students' clinical medical knowledge after Step 1 went to pass/fail. Eighty-four percent of these clerkship directors did not compare student performance on clerkship standardized exams before and after Step 1 score changes. We found no significant relationship between Step 1 timing and student performance. CONCLUSIONS: This study represents an early description of family medicine clerkship directors' perceived observations of the impact of Step 1 scoring changes on student performance. Continued investigation of the effects of USMLE Step 1 pass/fail scoring should occur.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".