Does the Medical College Admission Test (MCAT) predict licensing examination performance in the Canadian context?
Bibliographic record
Abstract
BACKGROUND: Research on the predictive validity of the Medical College Admissions Test (MCAT) on licensing examination performance is varied in its conclusions, with only a few studies examining this relationship in a Canadian context. We assessed the predictive validity of the MCAT on successful performance on the Medical Council of Canada Qualifying Examination (MCCQE) Part 1 by students attending the Cumming School of Medicine. METHODS: Prospective data were collected on MCAT score and sub-section scores, MCCQE decision, multiple mini interview (MMI) performance, gender, and age. The cohort was divided into a derivation cohort (2013 and 2014) and validation cohort (2015 and 2016). Students were dichotomized into pass or fail on MCCQE. Multiple logistic regression in which our dependent variable was MCCQE Part I examination success at the first attempt was used, and potential explanatory variables were age, gender, MCAT total score, and sub-scores for the biological sciences (MCAT-BS), physical sciences, and verbal reasoning, GPA, and MMI ratings. RESULTS: For the derivation cohort MCAT-BS was associated with success on the MCCQE Part I. The odds ratio for this association of 1.37 (95% confidence interval [1.01, 1.85], p = 0.04). When we applied the MCAT-BS to our validation cohort the odds ratio of MCCQE Part I examination success was 1.42 [1.10, 1.83], p = 0.007) and the area under the ROC curve was 0.66 [0.54, 0.79]). CONCLUSION: The MCAT-BS predicted successful performance on the MCCQE Part 1 Examination in the Canadian 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 | 0.004 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.122 | 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".