Impact of sociodemographic characteristics of applicants in multiple mini-interviews
Bibliographic record
Abstract
BACKGROUND: Multiple mini-interviews (MMI) are commonly used for medical school admission. This study aimed to assess if sociodemographic characteristics are associated with MMI performance, and how they may act as barriers or enablers to communication in MMI. METHODS: This mixed-method study combined data from a sociodemographic questionnaire, MMI scores, semi-structured interviews and focus groups with applicants and assessors. Quantitative and qualitative data were analyzed using multiple linear regression and a thematic framework analysis. RESULTS: 0.086) demonstrated that being age 25-29 (β = 0.11, p = 0.001), female and a French-speaker (β = 0.22, p = 0.003) were associated with better MMI scores. Having an Asian-born parent was associated with a lower score (β = -0.12, p < 0.001). Candidates reporting a higher family income had higher MMI scores. In the qualitative data, participants discussed how maturity and financial support improved life experiences, how language could act as a barrier, and how ethnocultural differences could lead to misunderstandings. CONCLUSION: Age, gender, ethnicity, socioeconomic status and language seem to be associated with applicants' MMI scores because of perceived differences in communications skills and life experiences. Monitoring this association may provide guidance to improve fairness of MMI stations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".