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Record W2580402349 · doi:10.1111/medu.13222

Multiple mini‐interview predictive validity for performance on a pharmacy licensing examination

2017· article· en· W2580402349 on OpenAlexaffabout
Andrea Cameron, Linda MacKeigan, Nicholas Mitsakakis, John Pugsley

Bibliographic record

VenueMedical Education · 2017
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsConference Board of CanadaUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsPharmacyLicensurePredictive validityMedicineContext (archaeology)Objective structured clinical examinationTest (biology)CertificationFamily medicinePsychologyClinical psychologyMedical education

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.038
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.136
GPT teacher head0.445
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2017
Admission routes2
Has abstractyes

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