The Effect of Incorporating Normative Data into a Criterion-Referenced Standard Setting in Medical Education
Bibliographic record
Abstract
PURPOSE: Determining standards for assessing clinical performance is a controversial issue. Purely item-based methods such as the Angoff method often produce unrealistic judgments, even when used by experienced judges. The rather unstudied compromise methods combine absolute and relative judgments and thereby incorporate normative data into criterion-based standard-setting judgments. The purpose of this study was to compare the quality and implications of standards set by three methods used for the Objective Structured Clinical Examination (OSCE). METHOD: Ninety-six judges set standards for 36 surgical year-4 undergraduate OSCE stations. All judges had normative student performance data when judgments were made with the Angoff, Ebel, or Hofstee methods. RESULTS: The Hofstee method gave more realistic cutoff scores and standard errors and better Meskauskas and Jaeger indices than the Angoff and Ebel methods. CONCLUSIONS: Medical educators setting standards for an OSCE should consider adopting the Hofstee method.
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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 | 0.486 | 0.806 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".