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Multiple mini‐interviews versus traditional interviews: stakeholder acceptability comparison

2009· article· en· W2161479585 on OpenAlex
Saleem Razack, Sonia Faremo, France Drolet, Linda Snell, Jeffrey Wiseman, Joyce Pickering

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueMedical Education · 2009
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyMedical educationMultivariate analysis of varianceStakeholderApplied psychologyPerceptionFamily medicineSocial psychologyMedicineComputer science

Abstract

fetched live from OpenAlex

CONTEXT: The McGill University Faculty of Medicine undertook a pilot, simulation-based multiple mini-interview (MMI) for medical school applicant selection, which ran simultaneously with traditional unstructured interviews (all applicants underwent both processes). This paper examines major stakeholder (applicants and evaluators) opinions towards the MMI compared with traditional interviews, including perceptions about the feasibility and utility of the MMI. METHODS: A total of 100 candidates applying to McGill University Medical School were enrolled in the pilot comparison of the MMI with the traditional, unstructured interview. Applicants' opinions were obtained by questionnaire shortly after the process (for all applicants) and approximately 6 months after the interviews (for non-accepted applicants). Evaluators' perceptions were also surveyed. Questionnaires contained both quantitative items and space for qualitative impressions. Descriptive statistics, repeated measures analysis of variance (manova) and analysis of the topics raised in written comments were conducted. RESULTS: Univariate analyses of response scores revealed statistically significant differences, with the MMI rated more highly than the traditional interview on fairness, imposition of stress and effectiveness as a measurement tool. Compared with the traditional interview, applicants also felt the MMI: (i) allowed them to be competitive; (ii) was enjoyable, and (iii) was often a favourite part of their interview experience. It should be noted that applicants were aware that their MMI score would be included in their overall interview rating. Written comments were positive with regard to, for example, fairness, the provision of opportunities to show one's strengths, and appreciation of the fidelity of the simulations. Evaluators' responses were in agreement with applicants' responses, albeit that overall they expressed more caution about the MMI. CONCLUSIONS: Results suggest the MMI is a promising selection tool from the point of view of both applicants and evaluators. Both groups expressed concerns, but overall the response was favourable for the MMI in comparison with traditional interviews, and the MMI has been adopted by McGill University's medical school.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.515
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0890.000

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.

Opus teacher head0.211
GPT teacher head0.427
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it