The Influence of Testing Context and Clinical Rotation Order on Students’ OSCE Performance
Bibliographic record
Abstract
PURPOSE: To investigate the influence of testing context and rotation order on third-year medical student performance on a common objective structured clinical examination (OSCE) station in both obstetrics-gynecology (ob-gyn) and psychiatry rotations. METHOD: Archival OSCE performance data (in the form of a 25-item binary content checklist) from one class of third-year medical students (n = 141) at Saint Louis University (2002-03) were aggregated and analyzed. RESULTS: Despite the fact that the station was identical in both OSCEs, students were, in general, less likely to inquire about ob-gyn issues on the psychiatry OSCE and less likely to inquire about psychiatric issues on the ob-gyn OSCE, regardless of order of rotation. Order did have a positive effect on some results, such that students were more likely to mention menopause and vaginal dryness on the psychiatry OSCE if they had already had the ob-gyn rotation. CONCLUSION: The testing context may influence student approaches to patients in ways that bias their collection and interpretation of information. OSCE evaluations may better approximate true clinical context and complexity by presenting case scenarios that reflect a broader range of diagnostic possibilities than those limited to the recently completed rotation.
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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 | 0.008 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".