Performance of the Ebel standard-setting method for the spring 2019 Royal College of Physicians and Surgeons of Canada internal medicine certification examination consisting of multiple-choice questions
Bibliographic record
Abstract
PURPOSE: It aimed to know the performance of the Ebel standard-setting method in in spring 2019 Royal College of Physicians and Surgeons of Canada internal medicine certification examination consisted of multiple-choice questions. Specifically followings were searched: the inter-rater agreement; the correlation between Ebel scores and item facility indices; raters' knowledge of correct answers' impact on the Ebel score; and affection of rater's specialty on theinter-rater agreement and Ebel scores. METHODS: Data were drawn from a Royal College of Physicians and Surgeons of Canada certification exam. Ebel's method was applied to 203 MCQs by 49 raters. Facility indices came from 194 candidates. We computed Fleiss' kappa and the Pearson correlation between Ebel scores and item facility indices. We investigated differences in the Ebel score (correct answers provided or not) and differences between internists and other specialists with t-tests. RESULTS: Kappa was below 0.15 for facility and relevance. The correlation between Ebel scores and facility indices was low when correct answers were provided and negligible when they were not. The Ebel score was the same, whether the correct answers were provided or not. Inter-rater agreement and Ebel scores was not differentbetween internists and other specialists. CONCLUSION: Inter-rater agreement and correlations between item Ebel scores and facility indices wee consistently low; furthermore, raters' knowledge of correct answer and rater specialty had no effect on Ebel scores in the present setting.
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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 | 0.039 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".