Multiple mini‐interview predictive validity for performance on a pharmacy licensing examination
Bibliographic record
Abstract
CONTEXT: Predictive validity studies on the use of the multiple mini-interview (MMI) have been primarily in medicine. OBJECTIVES: This study sought to determine the predictive validity of the MMI for performance within a pharmacy programme and on the Pharmacy Examining Board of Canada (PEBC) Qualifying Examination for licensure, and to compare the predictive validity of the MMI with that of pre-pharmacy grade point average (GPA) and Pharmacy College Admission Test (PCAT) score. METHODS: Admissions data for 223 graduates of the pharmacy programme at the University of Toronto were matched to programme and licensure outcome measures. Multiple linear regression assessed the predictive ability of the MMI, pre-pharmacy GPA, PCAT and covariates for performance in final-year experiential rotations, cumulative GPA (cGPA) and PEBC-MCQ (multiple-choice question examination) and PEBC-OSCE (objective structured clinical examination) overall and subcomponent scores. RESULTS: The PCAT, pre-pharmacy GPA and age significantly predicted the PEBC-MCQ overall score. The MMI was the only significant predictor of overall score on the PEBC-OSCE (β = 0.17, p = 0.02); it also predicted communication and performance subscores. Scores on the PCAT and female gender predicted the communication subscore. Pre-pharmacy GPA, age and female gender significantly predicted cGPA. The MMI was the only significant predictor of institutional/ambulatory rotation score (β = 0.26, p = 0.00). CONCLUSIONS: The MMI, designed to measure non-academic attributes including communication, motivation and problem-solving skills, was the only admissions tool with significant predictive validity for performance on the PEBC-OSCE national pharmacy certification examination and in an institutional/ambulatory rotation. These findings, from a single cohort of undergraduates, provide the first report of the predictive validity of the MMI for performance on pharmacy licensure examinations and thereby strengthen the evidence for its use in health professions selection. Prior university academic performance significantly predicted cGPA and performance on the PEBC-MCQ. Performance on the PCAT also predicted PEBC-MCQ results.
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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 | 0.001 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".