Temporal stability of objective structured clinical exams: a longitudinal study employing item response theory
Bibliographic record
Abstract
BACKGROUND: The objective structure clinical examination (OSCE) has been used since the early 1970s for assessing clinical competence. There are very few studies that have examined the psychometric stability of the stations that are used repeatedly with different samples. The purpose of the present study was to assess the stability of objective structured clinical exams (OSCEs) employing the same stations used over time but with a different sample of candidates, SPs, and examiners. METHODS: At Time 1, 191 candidates and at Time 2 (one year apart), 236 candidates participated in a 10-station OSCE; 6 of the same stations were used in both years. Generalizability analyses (Ep2) were conducted. Employing item response analyses, test characteristic curves (TCC) were derived for each of the 6 stations for a 2-parameter model. The TCCs were compared across the two years, Time 1 and 2. RESULTS: The Ep2 of the OSCEs exceeded.70. Standardized thetas (θ) and discriminations were equivalent for the same station across the two year period indicating equivalent TCCs for a 2-parameter model. CONCLUSION: The 6 OSCE stations used by the AIMG program over two years have adequate internal consistency reliability, stable generalizability (Ep2) and equivalent test characteristics. The process of assessment employed for IMG's are stable OSCE stations that may be used several times over without compromising psychometric properties.With careful security, high-stakes OSCEs may use the same stations that have high internal consistency and generalizability repeatedly as the psychometric properties are stable over several years with different samples of candidates.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".