Traditional Panel Interview versus Multiple Mini-Interview (MMI) in Medical School Admissions: Does Performance differ by Age, Gender, Urban or Rural, or Socioeconomic Status (Findings from one medical school)
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Introduction:Globally, medical schools are trying to widen access and to increase the diversity of their student body to be more representative of the population and to meet the future heath care needs of society. Selection methods must not disadvantage the applicants from priority groups. In Memorial University’s Faculty of Medicine, rural applicants and applicants from low socioeconomic status are priority groups. Methods:Since 2013, Memorial University has used a combination of traditional panel interviews and MMIs to interview candidates for medical school. We wondered whether applicants who participate in this medical school interview process perform differently on the MMIs compared to the traditional panel interview process and whether performance differs on either of the two interview processes based on age, sex, origin(urban or rural), or socioeconomic status.Results:The mean score on the traditional panel interview was higher than that on the MMI. Females scored higher than males on both the traditional panel interview and the MMI. Applicants aged 22 and younger performed worse on both the traditional panel interview and the MMI than the other age groups. Neighborhood socioeconomic status, and urban/rural living status were not significantly related with applicants’ performance on the traditional panel interview or MMI.Discussion:The type of interview is not disadvantaging applicants from Memorial University’s priority areas.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".