A Predictive and Construct Validity Study of a High-Stakes Objective Clinical Examination for Assessing the Clinical Competence of International Medical Graduates
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to investigate the predictive and construct validity of a high-stakes objective structured clinical examination (OSCE) used to select candidates for a 3-month clinical rotation to assess practice-readiness status. SUMMARY: Analyses were undertaken to establish the reliability and validity of the OSCE. The generalizability coefficient (Ep(2)) for the assessment scores (checklist, global, and total) were all high, ranging from 0.73 to 0.84. Two discriminant analyses (promotion to the 3-month rotation and pass/fail status on the rotation) provided evidence of predictive validity with a 100% correct classification rate in the pass/fail rotation results. Factor analysis results provided evidence of construct validity with four factors identified: Clinical Skills, Internal Medicine, General Medical Knowledge, and Counseling. The known group differences between licensing status and residency experience also provided evidence of construct validity. CONCLUSIONS: The results are encouraging for the predictive and construct validity of the OSCE as an assessment of clinical competence.
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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 | 0.017 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".