Measuring and correcting staff variability in large-scale OSCEs
Bibliographic record
Abstract
CONTEXT: Objective Structured Clinical Examinations (OSCEs) are an increasingly popular evaluation modality for medical students. While the face-to-face interaction allows for more in-depth assessment, it may cause standardization problems. Methods to quantify, limit or adjust for examiner effects are needed. METHODS: -year medical students at Université Paris Cité in the 2022-2023 academic year. Sessions had five stations each, and one of the three sessions was scored by consensus by two raters (rather than one). We report OSCEs' longitudinal consistency for one of the classes and staff-related and student variability by session. We also propose a statistical method to adjust for inter-rater variability by deriving a statistical random student effect that accounts for staff-related and station random effects. RESULTS: ), with staff-level standard errors approximately doubled compared to chance. With mixed models, staff-related heterogeneity explained respectively 11.4%, 11.6%, and 4.7% of station score variance (95% confidence intervals, 9.5-13.8, 9.7-14.1, and 3.9-5.8, respectively) with 1, 1 and 2 raters, suggesting a moderating effect of consensus grading. Student random effects explained a small proportion of variance, respectively 8.8%, 11.3%, and 9.6% (8.0-9.7, 10.3-12.4, and 8.7-10.5), and this low amount of signal resulted in student rankings being no more consistent over time with this metric, rather than with average scores (p=0.45). CONCLUSION: Staff variability impacts OSCE scores as much as student variability, and the former can be reduced with dual assessment or adjusted for with mixed models. Both are small compared to unmeasured sources of variability, making them difficult to capture consistently.
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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 | 0.124 | 0.327 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".