Multivariate Predictive-discriminatory Model of Academic Performance in Medical Students. A Mixed Longitudinal Study.

Reported studies on individual differences related to academic performance based on personality aspects describe self-efficiency, motivation and self-control as the main indicators of academic performance. Students who are motivated tend to have a better academic performance (AP) than peers with less motivation. On the other hand, differences in the environment related to academic performance have been linked to social aspects, among which are: family environment, family socioeconomic status and academic socialization. A constant factor in aspects of the environment that seems to permeate and impose itself on others is the socioeconomic one. About the personal factors of medical students, it has been found that university students who report stress caused by socioeconomic factors are more likely to have some psychological morbidity. There are certain ways to deal with stress that are related to better performance. Due to the complexity of the study of AP, it is necessary to develop holistic models that allow a better approach to reality. The educational research work related to academic disillusionment has been aimed at determining the factors involved in it. However, the methodological approach that has been used to document these works focuses on reductionism, where each variable that is studied and correlated independently with the AP. Although, this approach has provided valuable information, it is necessary to postulate holistic models that allow a better approach to reality. Although, there are published works on the correlation of the factors that can influence the academic performance of medical students of the Faculty of Medicine of the UNAM, no multivariate predictive models have been developed that include the main psychosocial factors and the resilience of students and how they impact academic performance; and also, study their behavior throughout a school year of the career, using stochastic models that allow characterizing a succession of random variab