ArticleAdvances in medical education and practice2011
Predicting performance at medical school: can we identify at-risk students?
Article in Advances in medical education and practice, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Factors influencing the late submission of assignments in a medical university environment: a qualitative case study.Medical education online · 2025Article
- Guidelines: The dos, don'ts and don't knows of remediation in medical education.Perspectives on medical education · 2019Article
- Performance of first-year health sciences students in a large, diverse, multidisciplinary, first-semester, physiology service module.Advances in physiology education · 2014Article
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Authors and funding
2 authors.
Funding
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Abstract
backgroundThe purpose of this study was to examine the predictive potential of multiple indicators (eg, preadmission scores, unit, module and clerkship grades, course and examination scores) on academic performance at medical school, with a view to identifying students at risk.
methodsAn analysis was undertaken of medical student grades in a 6-year medical school program at the Faculty of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates, over the past 14 years.
resultsWhile high school scores were significantly (P < 0.001) correlated with the final integrated examination, predictability was only 6.8%. Scores for the United Arab Emirates university placement assessment (Common Educational Proficiency Assessment) were only slightly more promising as predictors with 14.9% predictability for the final integrated examination. Each unit or module in the first four years was highly correlated with the next unit or module, with 25%-60% predictability. Course examination scores (end of years 2, 4, and 6) were significantly correlated (P < 0.001) with the average scores in that 2-year period (59.3%, 64.8%, and 55.8% predictability, respectively). Final integrated examination scores were significantly correlated (P < 0.001) with National Board of Medical Examiners scores (35% predictability). Multivariate linear regression identified key grades with the greatest predictability of the final integrated examination score at three stages in the program.
conclusionThis study has demonstrated that it may be possible to identify "at-risk" students relatively early in their studies through continuous data archiving and regular analysis. The data analysis techniques used in this study are not unique to this institution.
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