Development and Assessment of the Multiple Mini-Interview in a School of Pharmacy Admissions Model
Bibliographic record
Abstract
Objective. To describe the development, implementation, and evaluation of the multiple mini-interview (MMI) within a PharmD admissions model. Methods. Demographic data and academic indicators were collected for all candidates who participated in Candidates’ Day (n = 253), along with the score for each MMI station criteria (7 stations). A survey was administered to all candidates that completed the MMI and another survey was administered to all interviewers to examine perceptions of the MMI. A three-facet multifaceted Rasch measurement (MFRM) analysis was completed to determine interviewer severity, candidate ability, and MMI station difficulty using data from Chapel Hill and Asheville. Data for the Asheville MFRM included both 2013-2014 and 2014-2015 admission cycles while the Chapel Hill MFRM analyzed data from the 2013-2014 cycle alone. Results. Analyses suggest that MMI stations assessed different attributes as designed, with Cronbach’s alpha for each station ranging from 0.90 to 0.95. All correlations between MMI station scores and academic indicators were negligible (rp ≤ 0.2). No significant differences in average station scores were found based on age, gender, or race. Surveys indicated the MMI was generally considered acceptable by candidates and faculty. The MFRMs found differences in candidate ability explained 36-45% of the variance in MMI scores while differences in interviewer severity explained 9-16% of the variance in MMI scores. Conclusion. This study provides additional support for the use of the MMI as an admissions tool in pharmacy education.
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 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 | 0.044 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".