In the medical education field, the prediction of variables that have an impact on the academic performance of students is highly important as supporting programs can be implemented to avoid dropouts or failing scores. Several studies have confirmed the relationship between student performance during the first months at college and the one afterwards; nevertheless, every medical school has its particularities. The objective is to develop a logistic regression model to predict first-year medical students' performance using academic, psychological and vocational variables as well as learning and strategies for self-motivation. The study is observational, transversal and descriptive. The study group consisted of 1205 first-year medical students. Participants completed questionnaires dealing with general knowledge, psychosocial factors, factors associated with career choice, as well as research and autoregulation strategies inventory. Participation was fully voluntary and the results were used under confidential agreement (NDA). The multiple regression model considered pleasure in academic background, percentage of checkmarks in general knowledge, perceived efficiency (categoric with 3 levels), aptitude, interest in biological sciences and health, follow stablished regulations, drive and the pursue of social prestige as covariables. We conclude that a logistic regression model to predict academic performance, mostly of those medical students under academic risk in the biomedical area, is an efficient tool as it allows valid conclusions for appropriate decision making.
In terms of predictive analytics, REF employed also a logistic regression model for the prediction of variables that have impact on the academic performance of first year biomedical students.
Logistic Regression Model for the Academic Performance of First-Year Medical Students in the Biomedical Area
Creative Education
Psychology