The Effect of Differential Weighting of Academics, Experiences, and Competencies Measured by Multiple Mini Interview (MMI) on Race and Ethnicity of Cohorts Accepted to One Medical School
Bibliographic record
Abstract
PURPOSE: To examine whether academic scores, experience scores, and Multiple Mini Interview (MMI) core personal competencies scores vary across applicants' self-reported ethnicities, and whether changes in weighting of scores would alter the proportion of ethnicities underrepresented in medicine (URIM) in the entering class composition. METHOD: This study analyzed retrospective data from 1,339 applicants to the Rutgers Robert Wood Johnson Medical School interviewed for entering classes 2011-2013. Data analyzed included two academic scores-grade point average (GPA) and Medical College Admission Test (MCAT)-service/clinical/research (SCR) scores, and MMI scores. Independent-samples t tests evaluated whether URIM ethnicities differed from non-URIM across GPA, MCAT, SCR, and MMI scores. A series of "what-if" analyses were conducted to determine whether alternative weighting methods would have changed final admissions decisions and entering class composition. RESULTS: URIM applicants had significantly lower GPAs (P < .001), MCATs (P < .001), and SCR scores (P < .001). However, this pattern was not found with MMI score (non-URIM 10.4 [1.6], URIM 10.4 [1.3], P = .55). Alternative weighting analyses show that including academic/experiential scores impacts the percentage of URIM acceptances. URIM acceptance rate declined from 57% (100% MMI) to 43% (10% GPA/10% MCAT/10% SCR/70% MMI), 39% (30% GPA/70% MMI), to as low as 22% (50% MCAT/50% MMI). CONCLUSIONS: Sole reliance on the MMI for final admissions decisions, after threshold academic/experiential preparation are met, promotes diversity with the accepted applicant pool; weighting of "the numbers" or what is written about the application may decrease the acceptance of URIM applicants.
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 | 0.009 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".