ArticleBMC medical education2019
Struggling with strugglers: using data from selection tools for early identification of medical students at risk of failure.
Article in BMC medical education, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Predicting general practice trainee summative fellowship exam performance using commencement-of-training formative assessments: a retrospective cohort study.BMC medical education · 2026Article
- Enhancing graduate education assessment: a machine learning-based classification of academic performance in medical students.BMC medical education · 2026Article
- The Doctor of Physical Therapy Admission Test (DPTAT): A Vision for a Primary Care, Doctoring Profession.Medical science educator · 2025Article
- Ambiguous motivations in medical school applicants: a retrospective study from Japan.Medical education online · 2025Article
- The Predictive Power of Short Answer Questions in Undergraduate Medical Education Progress Difficulty.Medical science educator · 2025Article
- Relationship between admission selection tools and student attrition in the early years of medical school.Journal of Taibah University Medical Sciences · 2024Article
- Predicting students' academic progress and related attributes in first-year medical students: an analysis with artificial neural networks and Naïve Bayes.BMC medical education · 2024Article
- The validity of Engagement and Feedback Assessments (EFAs): identifying students at risk of failing.BMC medical education · 2023Article
- Prompt identification of struggling candidates in near peer-led basic life support training: piloting an online performance scoring system.BMC medical education · 2023Article
- Improving Learning and Study Strategies in Undergraduate Medical Students: A Pre-Post Study.Healthcare (Basel, Switzerland) · 2023Article
- The effect of face-to-face versus online learning on student performance in anatomy: an observational study using a causal inference approach.Discover education · 2023Article
- Assessment of dropout rates in the preclinical years and contributing factors: a study on one Thai medical school.BMC medical education · 2022Article
- Investigating the risk factors for academic difficulties in the medical programme at a South African university.BMC medical education · 2022Article
- Why do students struggle in their first year of medical school? A qualitative study of student voices.BMC medical education · 2022Article
- Mental health among Thai medical students: Preadmission evaluation and service utilization.Health science reports · 2021Article
- Factors leading to the academic failure of undergraduate medical students - Predict early to prevent.Pakistan journal of medical sciencesArticle
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Authors and funding
3 authors.
Funding
Abstract
backgroundStruggling medical students is an under-researched in medical education. It is known, however, that early identification is important for effective remediation. The aim of the study was to determine the predictive effect of medical school admission tools regarding whether a student will struggle academically.
methodsData comprise 700 students from the University of New South Wales undergraduate medical program. The main outcome of interest was whether these students struggled during this 6-year program; they were classified to be struggling they failed any end-of-phase examination but still graduated from the program. Discriminate Function Analysis (DFA) assessed whether their pre-admission academic achievement, Undergraduate Medicine Admission Test (UMAT) and interview scores had predictive effect regarding likelihood to struggle.
resultsA lower pre-admission academic achievement in the form of Australian Tertiary Admission Rank (ATAR) or Grade Point Average (GPA) were found to be the best positive predictors of whether a student was likely to struggle. Lower UMAT and poorer interview scores were found to have a comparatively much smaller predictive effect.
conclusionAlthough medical admission tests are widely used, medical school rarely use these data for educational purposes. The results of this study suggest admission test data can predict who among the admitted students is likely to struggle in the program. Educationally, this information is invaluable. These results indicate that pre-admission academic achievement can be used to predict which students are likely to struggle in an Australian undergraduate medicine program. Further research into predicting other types of struggling students as well as remediation methods are necessary.
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