Effect of Applicant Gender on Multiple Mini-Interview Admissions Score
Bibliographic record
Abstract
PURPOSE: Admissions criteria for physician assistant (PA) schools vary considerably, but they often involve a combination of academic measures, noncognitive "humanistic" qualities, and mission-related items. To address noncognitive attributes, some PA schools have implemented the multiple mini-interview (MMI) format developed at McMaster University in Canada. This research project looked at differences in interview scores by gender over 3 consecutive admissions cycles at one PA program. METHODS: Three years of pre-existing de-identified data gathered as part of the routine admissions process were analyzed retrospectively using SPSS-v25. RESULTS: Data were available for all interviewees for 2015, 2016, and 2017 (N = 350 total). Between-group differences were not statistically significant by gender. Reliability (Cronbach's α) was 0.865 for academic scoring and 0.694 for MMI scoring. DISCUSSION: Analysis of 3 years of admissions data from a single program did not show evidence for gender bias in MMI scores. Although this result is reassuring, it requires continued monitoring and replication.
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".