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A multivariate analysis examining the relationship between sociodemographic differences and UK graduates' performance on postgraduate medical exams.

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Title: A multivariate analysis examining the relationship between sociodemographic differences and UK graduates' performance on postgraduate medical exams.
Authors: Ellis, Ricky1 (AUTHOR) Rickyellis@nhs.net, Knapton, Andy2 (AUTHOR), Cannon, Jane2 (AUTHOR), Lee, Amanda J.1 (AUTHOR), Cleland, Jennifer3 (AUTHOR)
Source: Medical Teacher. Dec2025, Vol. 47 Issue 12, p2006-2020. 15p.
Subject Terms: *Educational tests & measurements, *Evaluation, *Statistical correlation, *Medical education, *Data analysis, *Retrospective studies, *Longitudinal method, *Medical schools, *Research, *Masters programs (Higher education), Research funding, Multiple regression analysis, Multivariate analysis, Descriptive statistics, Odds ratio, Medical records, Acquisition of data, Statistics, Sociodemographic factors, Data analysis software, Confidence intervals
Geographic Terms: Singapore, United Kingdom
Abstract: Background: Studies examining group-level performance (differential attainment, or DA) in UK postgraduate medical examinations have, to date, focused on a limited number of exams and sociodemographic factors and used relatively simple analyses. This limits understanding of the intersectionality of different characteristics in relation to performance on these critical assessments, required for progression through training and to consultant status. This study aimed to address these gaps by identifying independent predictors of success or failure for UK medical school graduates (UKGs) across UK postgraduate medical examinations. Methods: This retrospective cohort study used multivariate logistic regression to identify independent predictors of success or failure at each examination, accounting for prior academic attainment (at point of entry to medical school). Anonymised pass/fail at the first examination attempt data were extracted from the General Medical Council (GMC) database and analysed for all UKGs examination candidates between 2014 and 2020. Results: Between 2014–2020, 132,370 first examination attempts were made by UKGs, and 99,840 (75.4%) candidates passed at the first attempt. Multivariate analyses revealed that gender, age, ethnicity, religion, sexual orientation, disability, working less than full time and socioeconomic and educational background were all statistically significant independent predictors of success or failure in written and clinical examinations. The strongest independent predictors of failing written and/or clinical examinations were being from a minority ethnic background and having a registered disability. Conclusions: This large-scale study found that, even after accounting for prior academic attainment, there were significant differences in candidate examination pass rates according to key sociodemographic differences. The GMC, Medical Royal Colleges, and postgraduate training organisations now have a responsibility to use these data to guide future research and interventions that aim to reduce these attainment gaps. [ABSTRACT FROM AUTHOR]
Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Background: Studies examining group-level performance (differential attainment, or DA) in UK postgraduate medical examinations have, to date, focused on a limited number of exams and sociodemographic factors and used relatively simple analyses. This limits understanding of the intersectionality of different characteristics in relation to performance on these critical assessments, required for progression through training and to consultant status. This study aimed to address these gaps by identifying independent predictors of success or failure for UK medical school graduates (UKGs) across UK postgraduate medical examinations. Methods: This retrospective cohort study used multivariate logistic regression to identify independent predictors of success or failure at each examination, accounting for prior academic attainment (at point of entry to medical school). Anonymised pass/fail at the first examination attempt data were extracted from the General Medical Council (GMC) database and analysed for all UKGs examination candidates between 2014 and 2020. Results: Between 2014–2020, 132,370 first examination attempts were made by UKGs, and 99,840 (75.4%) candidates passed at the first attempt. Multivariate analyses revealed that gender, age, ethnicity, religion, sexual orientation, disability, working less than full time and socioeconomic and educational background were all statistically significant independent predictors of success or failure in written and clinical examinations. The strongest independent predictors of failing written and/or clinical examinations were being from a minority ethnic background and having a registered disability. Conclusions: This large-scale study found that, even after accounting for prior academic attainment, there were significant differences in candidate examination pass rates according to key sociodemographic differences. The GMC, Medical Royal Colleges, and postgraduate training organisations now have a responsibility to use these data to guide future research and interventions that aim to reduce these attainment gaps. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/0142159X.2025.2513426
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 15
        StartPage: 2006
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      – SubjectFull: Educational tests & measurements
        Type: general
      – SubjectFull: Evaluation
        Type: general
      – SubjectFull: Statistical correlation
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      – SubjectFull: Medical education
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      – SubjectFull: Data analysis
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      – SubjectFull: Retrospective studies
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      – SubjectFull: Longitudinal method
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      – SubjectFull: Medical schools
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      – SubjectFull: Odds ratio
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      – SubjectFull: United Kingdom
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              Text: Dec2025
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