ArticleBMJ open2023
Cross-sectional evaluation of an asynchronous multiple mini-interview (MMI) in selection to health professions training programmes with 10 principles for fairness built-in.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Designing AI-resilient admissions interviews for health professions training in the age of generative AI.NPJ digital medicine · 2026Article
- Leveling the playing field: evaluating measurement equivalence in MMIs between genders.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesWe aimed to explore the psychometric properties of the first known online asynchronous multiple mini-interview (MMI) designed for fairness with subgroup analyses by key characteristics, usability and acceptability.
designCross-discipline multimethod evaluation.
settingOne UK University.
participantsApplicants to nursing, midwifery and paramedic science undergraduate programmes during 2021-2022. PRIMARY, SECONDARY OUTCOME MEASURES: Psychometric properties (internal consistency, construct validity, dimensionality) were assessed using Cronbach's alpha (α), parallel analysis (PA), Schmid-Leiman transformation and ordinal confirmatory factor analysis (CFA). Usability and acceptability were evaluated using descriptive statistics and conventional content analysis.
methodsThe system was configured in a seven question 4 min MMI. Applicants' videorecorded their answers which were later assessed by interviewers and scores summed. Applicants and interviewers completed online evaluation questionnaires.
resultsPerformance data from 712 applicants determined good-excellent reliability for the asynchronous MMI (mean α 0.72) with similar results across subgroups (gender, age, disability/support needs, UK/non-UK). PA and factor analysis results suggested there were seven factors relating to the MMI questions with an underlying general factor that explained the variance in observed candidate responses. A CFA testing a seven-factor hierarchical model showed an excellent fit to the data (Confirmatory Fit Index=0.99), Tucker Lewis Index=0.99, root mean square error (RMSE) =0.034). Applicants (n=210) viewed the flexibility, relaxed environment and cost savings advantageous. Interviewers (n=65) reported the system to be intuitive, flexible with >70% time saved compared with face-to-face interviews. Reduced personal communication was cited as the principal disadvantage.
conclusionsWe found that the asynchronous MMI was reliable, time-efficient, fair and acceptable and building fairness in was lost-cost. These novel, insights are applicable across health professions selection internationally informing the future configuration of online interviews to ensure workforces represent the societies they serve.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.