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Does 'online confidence' predict application success and later academic performance in medical school? A UK-based national cohort study.

ABSTRACT: OBJECTIVES:The UK Clinical Aptitude Test (UKCAT) previously piloted an assessment of 'online confidence', where candidates were asked to indicate how confident they were with their answers. This study examines the relationship between these ratings, the odds of receiving an offer to study medicine and subsequent undergraduate academic performance. DESIGN:National cohort study. SETTING:UK undergraduate medical selection. PARTICIPANTS:56?785 UKCAT candidates who sat the test between 2013 and 2016 and provided valid responses to the online confidence pilot study. PRIMARY OUTCOME MEASURES:Two measures of 'online confidence' were derived: the well-established 'confidence bias', and; a novel 'confidence judgement' measure, developed using Item Response Theory in order to derive a more sophisticated metric of the ability to evaluate one's own performance on a task. Regression models investigated the relationships between these confidence measures, application success and academic performance. RESULTS:Online confidence was inversely related to cognitive performance. Relative underconfidence was associated with increased odds of receiving an offer to study medicine. For 'confidence bias' this effect was independent of potential confounders (OR 1.48, 1.15 to 1.91, p=0.002). While 'confidence judgement' was also a univariable predictor of application success (OR 1.22, 1.01 to 1.47, p=0.04), it was not an independent predictor. 'Confidence bias', but not 'confidence judgement', predicted the odds of passing the first year of university at the first attempt, independently of cognitive performance, with relative underconfidence positively related to academic success (OR 3.24, 1.08 to 9.72, p=0.04). No non-linear effects were observed, suggesting no 'sweet spot' exists in relation to online confidence and the outcomes studied. CONCLUSIONS:Applicants who either appear underconfident, or are better at judging their own performance on a task, are more likely to receive an offer to study medicine. However, online confidence estimates had limited ability to predict subsequent academic achievement. Moreover, there are practical challenges to evaluating online confidence in high-stakes selection.

SUBMITTER: Tiffin PA

PROVIDER: S-EPMC6937070 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Does 'online confidence' predict application success and later academic performance in medical school? A UK-based national cohort study.

Tiffin Paul A PA Paton Lewis W LW

BMJ open 20191229 12

<h4>Objectives</h4>The UK Clinical Aptitude Test (UKCAT) previously piloted an assessment of 'online confidence', where candidates were asked to indicate how confident they were with their answers. This study examines the relationship between these ratings, the odds of receiving an offer to study medicine and subsequent undergraduate academic performance.<h4>Design</h4>National cohort study.<h4>Setting</h4>UK undergraduate medical selection.<h4>Participants</h4>56 785 UKCAT candidates who sat th ...[more]

PMID: 31888945

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Project description: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.

Project description:BackgroundSelection of medical students in the UK is still largely based on prior academic achievement, although doubts have been expressed as to whether performance in earlier life is predictive of outcomes later in medical school or post-graduate education. This study analyses data from five longitudinal studies of UK medical students and doctors from the early 1970s until the early 2000s. Two of the studies used the AH5, a group test of general intelligence (that is, intellectual aptitude). Sex and ethnic differences were also analyzed in light of the changing demographics of medical students over the past decades.MethodsData from five cohort studies were available: the Westminster Study (began clinical studies from 1975 to 1982), the 1980, 1985, and 1990 cohort studies (entered medical school in 1981, 1986, and 1991), and the University College London Medical School (UCLMS) Cohort Study (entered clinical studies in 2005 and 2006). Different studies had different outcome measures, but most had performance on basic medical sciences and clinical examinations at medical school, performance in Membership of the Royal Colleges of Physicians (MRCP(UK)) examinations, and being on the General Medical Council Specialist Register.ResultsCorrelation matrices and path analyses are presented. There were robust correlations across different years at medical school, and medical school performance also predicted MRCP(UK) performance and being on the GMC Specialist Register. A-levels correlated somewhat less with undergraduate and post-graduate performance, but there was restriction of range in entrants. General Certificate of Secondary Education (GCSE)/O-level results also predicted undergraduate and post-graduate outcomes, but less so than did A-level results, but there may be incremental validity for clinical and post-graduate performance. The AH5 had some significant correlations with outcome, but they were inconsistent. Sex and ethnicity also had predictive effects on measures of educational attainment, undergraduate, and post-graduate performance. Women performed better in assessments but were less likely to be on the Specialist Register. Non-white participants generally underperformed in undergraduate and post-graduate assessments, but were equally likely to be on the Specialist Register. There was a suggestion of smaller ethnicity effects in earlier studies.ConclusionsThe existence of the Academic Backbone concept is strongly supported, with attainment at secondary school predicting performance in undergraduate and post-graduate medical assessments, and the effects spanning many years. The Academic Backbone is conceptualized in terms of the development of more sophisticated underlying structures of knowledge ('cognitive capital' and 'medical capital'). The Academic Backbone provides strong support for using measures of educational attainment, particularly A-levels, in student selection.

Project description:ObjectiveQuantify differential attainment by ethnicity in undergraduate medical assessments and evaluate whether institutional efforts to reduce the attainment gap have had impact.DesignObservational cohort study.SettingA single UK MBBS medical programme.ParticipantsPseudonymised data of adults aged ≥18 years enrolled in one of the UK MBBS medical programmes between 2012 and 2018. Ethnicity was self-declared during enrolment as White, Asian, Black, mixed and other.Main outcome measureModule mark (distinction, merit, pass, fail) graded according to a variety of assessments, including single best answer examinations, objective structured clinical examinations and coursework submissions. All modular assessments are graded as a percentage. Logistic regression models were used to calculate relative risk ratio to study the association between ethnicity and attainment gap over a calendar and scholastic year. Models were adjusted for age, gender, social deprivation and scholastic year of study.Results3714 student records were included. In the sample, 2134 students (57%) were non-white. The proportion of non-white students increased from 2007 (49%) to 2018 (70%). Mean age was 18 (IQR 18-21) and 56.6% were females. Higher proportion of non-white students 218 (24.8%) were from more deprived backgrounds versus white 76 (14.8%). Compared with non-white, there were no significant differences in the proportion of students failing assessments. However, white students were more likely to achieve merit (relative risk ratio 1.29 (95% CI 1.08 to 1.45)) or distinction (1.69 (95% CI 1.37 to 2.08)). Differences in attainment gap have remained unchanged over time, and for black students, attainment gap grew between their first and final year of study.ConclusionA similar proportion (97%) of non-white and white students had a passing score, but attainment gap for higher grades persists over years despite widespread efforts in medical schools to diminish the attainment gap linked to ethnicity. Our findings are from a single institution, thus affecting generalisability.

Project description:The acceptance of students to a medical school places a considerable emphasis on performance in standardized tests and undergraduate grade point average (uGPA). Traditionally, applicants may be judged as a homogeneous population according to simple quantitative thresholds that implicitly assume a linear relationship between scores and academic success. This 'one-size-fits-all' approach ignores the notion that individuals may show distinct patterns of achievement and follow diverse paths to success. In this study, we examined a dataset composed of 53 variables extracted from the admissions application records of 1,088 students matriculating to NYU School of Medicine between the years 2006-2014. We defined training and test groups and applied K-means clustering to search for distinct groups of applicants. Building an optimized logistic regression model, we then tested the predictive value of this clustering for estimating the success of applicants in medical school, aggregating eight performance measures during the subsequent medical school training as a success factor. We found evidence for four distinct clusters of students-we termed 'signatures'-which differ most substantially according to the absolute level of the applicant's uGPA and its trajectory over the course of undergraduate education. The 'risers' signature showed a relatively higher uGPA and also steeper trajectory; the other signatures showed each remaining combination of these two main factors: 'improvers' relatively lower uGPA, steeper trajectory; 'solids' higher uGPA, flatter trajectory; 'statics' both lower uGPA and flatter trajectory. Examining the success index across signatures, we found that the risers and the statics have significantly higher and lower likelihood of quantifiable success in medical school, respectively. We also found that each signature has a unique set of features that correlate with its success in medical school. The big data approach presented here can more sensitively uncover success potential since it takes into account the inherent heterogeneity within the student population.

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Does 'online confidence' predict application success and later academic performance in medical school? A UK-based national cohort study.

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Does 'online confidence' predict application success and later academic performance in medical school? A UK-based national cohort study.

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