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[A predictive model for academic risk in medicine students].
Peter Mccoll-Calvo1, Jessica Goset-Poblete1, Felipe Martínez-Lomakin1
1Facultad de Medicina, Universidad Andrés Bello sede, Viña del Mar, Chile.
Revista Medica De Chile
|June 23, 2022
Summary
Medical students
Area of Science:
- Neuroscience
- Medical Education
- Psychology
Background:
- University academic performance is crucial.
- Current risk systems fail to identify at-risk medical students due to high entrance scores.
- A new approach is needed to detect academic risk in this population.
Purpose of the Study:
- To develop a predictive model for academic risk in first-year medical students.
- To utilize neuro-didactic variables for improved risk detection.
- To enhance academic support for medical students.
Main Methods:
- Prospective cohort study of 189 first-year medical students.
- Utilized questionnaires to assess executive functions and reward system variables.
- Employed logistic regression and classification tree analysis.
Main Results:
- Identified key predictors of academic risk with 74% accuracy (logistic regression).
- Classification tree achieved 68% sensitivity and 79% specificity.
- Significant variables included science, math, and emotional intelligence test scores, family pressure, and living situation.
Conclusions:
- Low science entrance exam scores are an initial indicator of academic risk.
- Reward system variables modulate this risk.
- Emotional intelligence is a critical factor in complex risk prediction.
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