Vive la Différence: The Freedom and Inherent Responsibilities When Designing and Implementing Multiple Mini-Interviews
Bibliographic record
Abstract
The literature on multiple mini-interviews (MMIs) is replete with heterogeneous study results related to the constructs measured, correlations with other measures, and demographic relationships. Rather than view these results as contradictory, the authors ask, What if all of the results are correct? They point out that the MMI is not an assessment tool but, rather, an assessment method. Design and implementation of locally conducted MMIs in medical school admissions processes should reflect local needs. As with other local assessments, MMIs should be considered separate from nationally conducted assessments that reflect more universal competencies. With the freedom to exercise unique values in locally constructed MMIs, individual institutions, or small bands of like-minded institutions, in parallel carry the responsibility to ensure local assessment tool validity.
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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 | 0.140 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.022 | 0.036 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".