Front row seat: The role MMI assessors play in widening access to medical school
Bibliographic record
Abstract
BACKGROUND: While many medical schools utilize the Multiple Mini-Interview (MMI) to help select a diverse student body, we know little about MMI assessors' roles. Do MMI assessors carry unique insights on widening access (WA) to medical school? Herein we discuss the hidden expertise and insights that assessors contribute to the conversation around WA. METHODS: Ten MMI assessors (1-10 years' experience) participated in semi-structured interviews exploring factors influencing equitable medical school recruitment. Given their thoughtfulness during initial interviews, we invited them for follow-up interviews to gain further insight into their perceived role in WA. Fourteen interviews were conducted and analyzed using a thematic analysis approach. RESULTS: Assessors expressed concerns with diversity in medicine; dissatisfaction with the status quo fueled their contributions to the selection process. Assessors advocated for greater diversity among the assessor pool, citing benefits for all students, not only those from underrepresented groups. They noted that good intentions were not enough and that medical schools can do more to include underrepresented groups' perspectives in the admissions process. CONCLUSION: Our analysis reveals that MMI assessors are committed to WA and make thoughtful contributions to the selection process. A medical school selection process, inclusive of assessors' expertise is an important step in WA.
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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 | 0.045 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".