Probing the effect of OSCE checklist length on inter-observer reliability and observer accuracy
Bibliographic record
Abstract
PURPOSE: The Objective Structured Clinical Examination (OSCE) is a widely employed tool for measuring clinical competence. In the drive toward comprehensive assessment, OSCE stations and checklists may become increasingly complex. The objective of this study was to probe inter-observer reliability and observer accuracy as a function of OSCE checklist length. METHOD: Study participants included emergency physicians and senior residents in Emergency Medicine at Dalhousie University. Participants watched an identical series of four, scripted, standardized videos enacting 10-min OSCE stations and completed corresponding assessment checklists. Each participating observer was provided with a random combination of two 40-item and two 20-item checklists. A panel of physicians scored the scenarios through repeated video review to determine the 'gold standard' checklist scores. RESULTS: Fifty-seven observers completed 228 assessment checklists. Mean observer accuracy ranged from 73 to 93% (14.6-18.7/20), with an overall accuracy of 86% (17.2/20), and inter-rater reliability range of 58-78%. After controlling for station and individual variation, no effect was observed regarding the number of checklist items on overall accuracy (p=0.2305). Consistency in ratings was calculated using intraclass correlation coefficient and demonstrated no significant difference in consistency between the 20- and 40-item checklists (ranged from 0.432 to 0.781, p-values from 0.56 to 0.73). CONCLUSIONS: The addition of 20 checklist items to a core list of 20 items in an OSCE assessment checklist does not appear to impact observer accuracy or inter-rater reliability.
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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 | 0.171 | 0.422 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".