Background Depression, anxiety, and stress (DAS) have been linked to poor academic outcomes. This study explores the relationships among DAS, academic engagement, dropout intentions, and academic performance — measured by Grade Point Average (GPA) — in medical students. It aims to understand how these factors relate to each other and predict academic performance. Methods Data were collected from 351 medical students (74.9 % female) through an online survey. The average age was 20.2 years. Psychometric instruments measured DAS, academic engagement, and dropout intentions. Structural equation modeling was used to test the relationships between these variables and their prediction of GPA. Results DAS negatively associated academic engagement (?=-0.501; p<0.001) and positively connected to dropout intentions (?=0.340; p<0.001). Academic engagement positively predicted GPA (?=0.298; p<0.001) and negatively associated with dropout intentions (?=-0.367; p<0.001). DAS had a nonsignificant direct effect on GPA (?=-0.008; p=0.912). However, DAS indirect effect — via academic engagement — on GPA and dropout intention was statistically significant. Limitations The study’s limitations include the use of a convenience sample and the collection of all variables — except GPA — at the same time point, which may affect the generalizability of the results. Conclusions The study supports the important role of DAS in associating with academic engagement and dropout intentions, which can predict GPA. Addressing DAS could enhance academic engagement and reduce dropout rates, leading to better academic performance.

The authors express their deepest thanks to all the students who took part in the study. Further, they would like to acknowledge the Portuguese National Distributed Computing Infrastructure for their support, and extend special thanks to João Pina, Jorge

Depression,Anxiety,Stress,Academic engagement,Dropout intentions,Academic performance,Medical students

• Clinical Medicine - Medical and Health Sciences
• Psychology - Social Sciences

This publication is an output of the following project(s):

• Bayesian Structural Equation Modeling: GPU processing, is it worth it?