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Cross-sectional evaluation of an asynchronous multiple mini-interview (MMI) in selection to health professions training programmes with 10 principles for fairness built-in.

Callwood, A; Harris, J; Gillam, L; Roberts, S; Kubacki, A; Christidis, A; Tiffin, PA (2023) Cross-sectional evaluation of an asynchronous multiple mini-interview (MMI) in selection to health professions training programmes with 10 principles for fairness built-in. BMJ Open, 13 (10). e074440. ISSN 2044-6055 https://doi.org/10.1136/bmjopen-2023-074440
SGUL Authors: Kubacki, Angela

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Abstract

OBJECTIVES: We 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. DESIGN: Cross-discipline multimethod evaluation. SETTING: One UK University. PARTICIPANTS: Applicants 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. METHODS: The 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. RESULTS: Performance 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. CONCLUSIONS: We 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.

Item Type: Article
Additional Information: © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
Keywords: health education, medical education & training, nursing care, Humans, Reproducibility of Results, Cross-Sectional Studies, School Admission Criteria, Communication, Health Occupations, Humans, Cross-Sectional Studies, Reproducibility of Results, Communication, Health Occupations, School Admission Criteria, 1103 Clinical Sciences, 1117 Public Health and Health Services, 1199 Other Medical and Health Sciences
SGUL Research Institute / Research Centre: Academic Structure > Institute of Medical & Biomedical Education (IMBE)
Academic Structure > Institute of Medical & Biomedical Education (IMBE) > Centre for Clinical Education (INMECE )
Journal or Publication Title: BMJ Open
ISSN: 2044-6055
Language: eng
Dates:
DateEvent
31 October 2023Published
18 September 2023Accepted
Publisher License: Creative Commons: Attribution 4.0
Projects:
Project IDFunderFunder ID
37043Innovate UKhttp://dx.doi.org/10.13039/501100006041
PubMed ID: 37907294
Go to PubMed abstract
URI: https://openaccess.sgul.ac.uk/id/eprint/116083
Publisher's version: https://doi.org/10.1136/bmjopen-2023-074440

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