Comparing Standard Setting Methods for Objective Structured Clinical Examinations in a Caribbean Medical School
Post-publication record
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Bibliographic record
Abstract
BACKGROUND: OSCE are widely used for assessing clinical skills training in medical schools. Use of traditional pass fail cut off yields wide variations in the results of different cohorts of students. This has led to a growing emphasis on the application of standard setting procedures in OSCEs. PURPOSE/AIM: The purpose of the study was comparing the utility, feasibility and appropriateness of 4 different standard setting methods with OSCEs at XUSOM. METHODS: A 15-station OSCE was administered to 173 students over 6 months. Five stations were conducted for each organ system (Respiratory, Gastrointestinal and Cardiovascular). Students were assessed for their clinical skills in 15 stations. Four different standard setting methods were applied and compared with a control (Traditional method) to establish cut off scores for pass/fail decisions. RESULTS: OSCE checklist scores revealed a Cronbach's alpha of 0.711, demonstrating acceptable level of internal consistency. About 13 of 15 OSCE stations performed well with "Alpha if deleted values" lower that 0.711 emphasizing the reliability of OSCE stations. The traditional standard setting method (cut off score of 70) resulted in highest failure rate. The Modified Angoff Method and Relative methods yielded the lowest failure rates, which were typically less than 10% for each system. Failure rates for the Borderline methods ranged from 28% to 57% across systems. CONCLUSIONS: In our study, Modified Angoff method and Borderline regression method have shown to be consistently reliable and practically suitable to provide acceptable cut-off score across different organ system. Therefore, an average of Modified Angoff Method and Borderline Regression Method appeared to provide an acceptable cutoff score in OSCE. Further studies, in high-stake clinical examinations, utilizing larger number of judges and OSCE stations are recommended to reinforce the validity of combining multiple methods for standard setting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".