Should Efforts in Favor of Medical Student Diversity Be Focused During Admissions or Farther Upstream?
Bibliographic record
Abstract
PURPOSE: Traditional medical school admissions assessment tools may be limiting diversity. This study investigates whether the Multiple Mini-Interview (MMI) is diversity-neutral and, if so, whether applying it with greater weight would dilute the anticipated negative impact of diversity-limiting admissions measures. METHOD: Interviewed applicants to six medical schools in 2008 and 2009 underwent MMI. Predictor variables of MMI scores, grade point average (GPA), and Medical College Admission Test (MCAT) scores were correlated with diversity measures of age, gender, size of community of origin, income level, and self-declared aboriginal status. A subset of the data was then combined with variable weight assigned to predictor variables to determine whether weighting during the applicant selection process would affect diversity among chosen applicants. RESULTS: MMI scores were unrelated to gender, size of community of origin, and income level. They correlated positively with age and negatively with aboriginal status. GPA and MCAT correlated negatively with age and aboriginal status, GPA correlated positively with income level, and MCAT correlated positively with size of community of origin. Even extreme combinations of MMI and GPA weightings failed to increase diversity among applicants who would be selected on the basis of weighted criteria. CONCLUSIONS: MMI could not neutralize the diversity-limiting properties of academic scores as selection criteria to interview. Using academic scores in this way causes range restriction, counteracting attempts to enhance diversity using downstream admissions selection measures such as MMI. Diversity efforts should instead be focused upstream. These results lend further support for the development of pipeline programs.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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".