Modification of an OSCE format to enhance patient continuity in a high-stakes assessment of clinical performance
Bibliographic record
Abstract
BACKGROUND: Traditional Objective Structured Clinical Examinations (OSCEs) are psychometrically sound but have the limitation of fragmenting complex clinical cases into brief stations. We describe a pilot study of a modified OSCE that attempts to balance a typical OSCE format with a semblance of a continuous, complex, patient case. METHODS: Two OSCE scenarios were developed. Each scenario involved a single standardized patient and was subdivided into three sequential 10 minute sections that assessed separate content areas and competencies. Twenty Canadian PGY-4 internal medicine trainees were assessed by trained examiner pairs during each OSCE scenario. Paired examiners rated participant performance independent of each other, on each section of each scenario using a validated global rating scale. Inter-rater reliabilities and Pearson correlations between ratings of the 3 sections of each scenario were calculated. A generalizability study was conducted. Participant and examiner satisfaction was surveyed. RESULTS: There was no main effect of section or scenario. Inter-rater reliability was acceptable. The g-coefficient was 0.68; four scenarios would achieve 0.80. Moderate correlations between sections of a scenario suggest a possible halo effect. The majority of examiners and participants felt that the modified OSCE provided a sense of patient continuity. CONCLUSIONS: The modified OSCE provides another approach to the assessment of clinical performance. It attempts to balance the advantages of a traditional OSCE with a sense of patient continuity.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".