Student mental wellbeing as an interconnected system: positioning academic, social, and coping processes.

Student mental wellbeing has become a growing concern in higher education, with increasing numbers of students reporting stress and psychological difficulties. Although academic factors, social environmental factors, and coping styles are all considered relevant, less is known about how these factors are interconnected in relation to mental wellbeing within a broader system. This study applied a network approach to examine how these factors were interconnected at the start of the academic year and how they were longitudinally associated later in the academic year. Survey data were collected among students at a Dutch university of applied sciences in November 2023 (T1; N = 497) and June 2024 (T2; n = 207). Data were collected on mental wellbeing, academic stressors, academic self-perceptions, coping styles and social environmental factors. A Gaussian Graphical Model was used to estimate the cross-sectional network at T1, and longitudinal associations from T1 to T2 were examined using regularized regression (LASSO). The findings showed that associations in the cross-sectional network were generally modest. Connections were mainly observed between conceptually related variables, including academic stressors, coping styles, and social environmental factors. Mental wellbeing showed few direct associations with the other constructs in the network, but both mental wellbeing and social support showed relatively high centrality, indicating that they were strongly connected within the overall network structure. Longitudinal analyses indicated substantial stability across the academic year, as most variables were primarily related to their own prior levels and only a limited number of associations between different variables were observed over time. These findings suggest that student mental wellbeing was embedded in a relatively stable system of related experiences. They also illustrate that cross-sectional variable network approaches may have limitations when conditional associations are weak and constructs overlap conceptually, particularly when used to draw conclusions about complex socio-ecological processes.