Effect of Detailed OSCE Score Reporting on Learning and Anxiety in Medical School
Bibliographic record
Abstract
INTRODUCTION: There is growing literature on increasing feedback from Objective Structured Clinical Examinations (OSCEs) and one approach is a score report. The purpose of this study was to implement and evaluate a score report for a second and fourth-year medical school OSCE. METHODS: We developed an electronic OSCE score report that displayed comments and performance by domain within and across stations (checklist items and rating scales were tagged to each domain). Our initial pilot released the score report after pass/fail decisions but subsequent iterations released the score report the same day as the exam. Our evaluation approach included both student surveys and focus groups. RESULTS: Students felt the OSCE score report was accurate, identified strengths and weaknesses, and would likely cause them to take future action, with second-year students more likely to act on the report than fourth year students. The thematic analysis revealed barriers and enablers to utilizing feedback as well as the power of the score report to reduce anxiety. CONCLUSIONS: Our OSCE score report was simple to develop and implement the same day as an OSCE with an overall positive response from students with respect to accuracy and ability to use the information for future learning.
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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 | 0.032 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".