A Model to Predict Student Failure in the First Year of the Undergraduate Medical Curriculum
To develop a model for the early and reliable prediction of students who fail to pass the first year of the undergraduate medical curriculum within two years after the start. 1819 medical students of five consecutive cohorts were included. By logistic regression analyses, predictions for failure in...
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Veröffentlicht in: | Health professions education 2017-06, Vol.3 (1), p.5-14 |
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Sprache: | eng |
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Zusammenfassung: | To develop a model for the early and reliable prediction of students who fail to pass the first year of the undergraduate medical curriculum within two years after the start.
1819 medical students of five consecutive cohorts were included. By logistic regression analyses, predictions for failure in the first-year curriculum were made at 0, 4, 6, 8, 10 and 12 months. Predictive variables included pre-admission variables such as age, gender, pre-university education GPA, the way students were selected, and post-admission variables such as number of credits obtained, degree of participation in exams, and exam success rate. Variables were only included if they contributed significantly to the model both for the five cohorts together and for each cohort separately. Students who had voluntarily withdrawn before a predictive moment were not included in the analyses.
Students who had passed all exams at 4 or 6 or 8 months (so-called "optimals") had a chance of 99% of passing the first-year curriculum. Within the group of non-optimals, at 6 months, failure to pass the first-year curriculum could be predicted with a specificity of 66.7% and a sensitivity of 84.5% by using the variable ׳passing 0 exams between 4 and 6 months׳. Specificity increased from the start till 6 months and remained constant afterwards.
The earliest moment with the highest specificity to predict student failure in the first-year curriculum seems to be at 6 months. However, additional factors are needed to improve this prediction or to bring forward the predictive moment. |
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ISSN: | 2452-3011 2452-3011 |
DOI: | 10.1016/j.hpe.2017.01.001 |