The Possible Impact of the MMI and GPA on Diversity
Bibliographic record
Abstract
To the Editor: In a recent article in Academic Medicine, Jerant et al1 found a statistically significant association between extroversion and performance on the Multiple Mini-Interview (MMI). Their data suggest that the MMI selects for individuals who are outgoing, interested in others, and skilled in the interpersonal domain—not altogether bad characteristics for health professionals. However, they viewed this finding as disquieting and suggested that the MMI “could contribute to reduced diversity of thoughts, attitudes, and behaviors in medical school classes,” although they acknowledged that this possibility requires further study. Studies by others have shown that a consistent relation also exists between undergraduate GPA and conscientiousness.2,3 Presumably, this would lead to a similar concern: The use of the GPA may preferentially select students with high motivation, achievement, and academic performance. Any selection instrument is effective only to the extent that it reduces diversity on some dimension that is, hopefully, related to future performance. In that respect, both the MMI and GPA represent a considerable advance over the personal interview, which was deliberately introduced at Harvard in the early 20th century to reduce ethnic diversity by preferentially selecting against Jewish applicants.4 Although it is no longer used for such discriminatory practices, the personal interview has been repeatedly demonstrated to have no relation to important educational outcomes.5,6 Geoffrey R. Norman, PhD Professor of clinical epidemiology and biostatistics, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada; [email protected] Kevin Eva, PhD Professor, University of British Columbia Faculty of Medicine, Vancouver, British Columbia, Canada. Mahan Kulasegaram PhD student, McMaster University Faculty of Health Sciences, Hamilton, Ontario, Canada.
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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 | 0.011 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".