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      The relationship between school type and academic performance at medical school: a national, multi-cohort study

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          Abstract

          Objectives

          Differential attainment in school examinations is one of the barriers to increasing student diversity in medicine. However, studies on the predictive validity of prior academic achievement and educational performance at medical school are contradictory, possibly due to single-site studies or studies which focus only on early years’ performance. To address these gaps, we examined the relationship between sociodemographic factors, including school type and average educational performance throughout medical school across a large number of diverse medical programmes.

          Methods

          This retrospective study analysed data from students who graduated from 33 UK medical schools between 2012 and 2013. We included candidates’ demographics, pre-entry grades (adjusted Universities and Colleges Admissions Service tariff scores) preadmission test scores (UK Clinical Aptitude Test (UKCAT) and Graduate Medical School Admissions Test (GAMSAT)) and used the UK Foundation Programme’s educational performance measure (EPM) decile as an outcome measure. Logistic regression was used to assess the independent relationship between students’ background characteristics and EPM ranking.

          Results

          Students from independent schools had significantly higher mean UKCAT scores (2535.1, SD=209.6) than students from state-funded schools (2506.1, SD=224.0, p<0.001). Similarly, students from independent schools came into medical school with significantly higher mean GAMSAT scores (63.9, SD=6.9) than students from state-funded schools (60.8, SD=7.1, p<0.001). However, students from state-funded schools were almost twice as likely (OR=2.01, 95% CI 1.49 to 2.73) to finish in the highest rank of the EPM ranking than those who attended independent schools.

          Conclusions

          This is the first large-scale study to examine directly the relationship between school type and overall performance at medical school. Our findings provide modest supportive evidence that, when students from independent and state schools enter with similar pre-entry grades, once in medical school, students from state-funded schools are likely to outperform students from independent schools. This evidence contributes to discussions around contextualising medical admission.

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          Most cited references47

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          Use of the extreme groups approach: a critical reexamination and new recommendations.

          Analysis of continuous variables sometimes proceeds by selecting individuals on the basis of extreme scores of a sample distribution and submitting only those extreme scores to further analysis. This sampling method is known as the extreme groups approach (EGA). EGA is often used to achieve greater statistical power in subsequent hypothesis tests. However, there are several largely unrecognized costs associated with EGA that must be considered. The authors illustrate the effects EGA can have on power, standardized effect size, reliability, model specification, and the interpretability of results. Finally, the authors discuss alternative procedures, as well as possible legitimate uses of EGA. The authors urge researchers, editors, reviewers, and consumers to carefully assess the extent to which EGA is an appropriate tool in their own research and in that of others.
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            Factors associated with success in medical school: systematic review of the literature.

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              Ethnicity and academic performance in UK trained doctors and medical students: systematic review and meta-analysis

              Objective To determine whether the ethnicity of UK trained doctors and medical students is related to their academic performance. Design Systematic review and meta-analysis. Data sources Online databases PubMed, Scopus, and ERIC; Google and Google Scholar; personal knowledge; backwards and forwards citations; specific searches of medical education journals and medical education conference abstracts. Study selection The included quantitative reports measured the performance of medical students or UK trained doctors from different ethnic groups in undergraduate or postgraduate assessments. Exclusions were non-UK assessments, only non-UK trained candidates, only self reported assessment data, only dropouts or another non-academic variable, obvious sampling bias, or insufficient details of ethnicity or outcomes. Results 23 reports comparing the academic performance of medical students and doctors from different ethnic groups were included. Meta-analyses of effects from 22 reports (n=23 742) indicated candidates of “non-white” ethnicity underperformed compared with white candidates (Cohen’s d=−0.42, 95% confidence interval −0.50 to −0.34; P<0.001). Effects in the same direction and of similar magnitude were found in meta-analyses of undergraduate assessments only, postgraduate assessments only, machine marked written assessments only, practical clinical assessments only, assessments with pass/fail outcomes only, assessments with continuous outcomes only, and in a meta-analysis of white v Asian candidates only. Heterogeneity was present in all meta-analyses. Conclusion Ethnic differences in academic performance are widespread across different medical schools, different types of exam, and in undergraduates and postgraduates. They have persisted for many years and cannot be dismissed as atypical or local problems. We need to recognise this as an issue that probably affects all of UK medical and higher education. More detailed information to track the problem as well as further research into its causes is required. Such actions are necessary to ensure a fair and just method of training and of assessing current and future doctors.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2017
                31 August 2017
                : 7
                : 8
                : e016291
                Affiliations
                [1 ] departmentInstitute of Education for Medical and Dental Sciences, School of Medicine, Medical Sciences & Nutrition , University of Aberdeen , Aberdeen, UK
                [2 ] NHS Education for Scotland and UK Foundation Programme , Aberdeen, UK
                [3 ] departmentMedical Statistics Team, School of Medicine, Medical Sciences & Nutrition , University of Aberdeen , Aberdeen, UK
                [4 ] UK Clinical Aptitude Test (UKCAT) Foundation, University of Nottingham , Nottingham, UK
                Author notes
                [Correspondence to ] Ben Kumwenda; r01bk15@ 123456abdn.ac.uk
                Author information
                http://orcid.org/0000-0003-1600-8229
                Article
                bmjopen-2017-016291
                10.1136/bmjopen-2017-016291
                5589012
                28860227
                99715ff7-e22a-4c65-afc0-1c701ff179a3
                © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                History
                : 05 February 2017
                : 04 July 2017
                : 12 July 2017
                Funding
                Funded by: UK Clinical Aptitude Test (UKCAT) Research Panel;
                Categories
                Medical Education and Training
                Research
                1506
                1709
                1357
                Custom metadata
                unlocked

                Medicine
                medical education,admissions,performance,widening access,predictive validity
                Medicine
                medical education, admissions, performance, widening access, predictive validity

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