Calibrating the Medical Council of Canada’s Qualifying Examination Part I using an integrated item response theory framework: a comparison of models and designs
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Bibliographic record
Abstract
PURPOSE: The aim of this research was to compare different methods of calibrating multiple choice question (MCQ) and clinical decision making (CDM) components for the Medical Council of Canada's Qualifying Examination Part I (MCCQEI) based on item response theory. METHODS: Our data consisted of test results from 8,213 first time applicants to MCCQEI in spring and fall 2010 and 2011 test administrations. The data set contained several thousand multiple choice items and several hundred CDM cases. Four dichotomous calibrations were run using BILOG-MG 3.0. All 3 mixed item format (dichotomous MCQ responses and polytomous CDM case scores) calibrations were conducted using PARSCALE 4. RESULTS: The 2-PL model had identical numbers of items with chi-square values at or below a Type I error rate of 0.01 (83/3,499 or 0.02). In all 3 polytomous models, whether the MCQs were either anchored or concurrently run with the CDM cases, results suggest very poor fit. All IRT abilities estimated from dichotomous calibration designs correlated very highly with each other. IRT-based pass-fail rates were extremely similar, not only across calibration designs and methods, but also with regard to the actual reported decision to candidates. The largest difference noted in pass rates was 4.78%, which occurred between the mixed format concurrent 2-PL graded response model (pass rate= 80.43%) and the dichotomous anchored 1-PL calibrations (pass rate= 85.21%). CONCLUSION: Simpler calibration designs with dichotomized items should be implemented. The dichotomous calibrations provided better fit of the item response matrix than more complex, polytomous calibrations.
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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 | 0.143 | 0.522 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it