Individual and psychosocial factors of mental health indicators among Slovak university students
Individual and psychosocial factors of mental health indicators among Slovak university students

Summary/Abstract: Background: Frequent surveys evaluating mental health risk factors for students and their resilience to stress during university studies seem to be very important for both monitoring and prevention purposes. The findings have shown that the association between stress and well-being, and stress and depressive feelings is not only direct but also mediated and moderated by other psychosocial variables. However, there still remains a lack of studies that would test comprehensive models exploring different sets of variables; such as gender, social support, self-regulation, perceived stress and resilience as well as analysis showing to what degree they contribute to the variance in health indicators. Furthermore, it might be even more important to address the underlying processes behind these associations and explain how (i.e. mediation) and under which conditions (i.e. moderation) these associations with mental health indicators operate. Objective: The first aim of this study was to explore how much variance in the health indicators, emotional wellbeing (EWB) and depressive symptoms (M-BDI), can be explained by a set of individual and psychosocial factors: gender, social support, self-regulation, perceived stress and resilience. Secondly, this study aimed to explore the indirect effect of perceived stress on mental health indicators through the resilience among university students. The final aim was to test whether this indirect effect is moderated by social support, or, in other words, whether it depends on the level of social support. Method: 237 students from four universities in Eastern Slovakia took part in this study (79.4% females, all aged 18–35, mean age 19.94, SD = 1.54). The collection of the data was part of the SLiCE (Student Life Cohort in Europe) research project. The SLiCE study has developed from previous collaborative research activities within the Cross-National Student Health Survey which was conducted in May 2008. The data came from the second round of this study. The selected universities provided e-mail addresses of all first year students who were asked to participate in the first round of the study. From 4,062 students, 814 provided data by completing an online questionnaire (response rate: 20.03%) and 237 also participated in the second round (response rate: 29.12% from the first round, 5.83% from all asked students). In total, 237 respondents provided data on all studied variables. Two measures were used as the indicators of mental health. The WHO-5 Well-being Index was used to measure emotional well-being. The second indicator of mental health was depressive symptoms measured by the modified version of the Beck Depression Inventory (M-BDI). Perceived stress was assessed by a short (4-item) version of the Perceived Stress Scale. Trait resilience was explored using the Connor-Davidson-Resilience Scale. Perceived social support was measured using the Multidimensional Scale of Perceived Social Support. In order to assess self-regulation skills, the Short Self-Regulation Questionnaire was used. The data analyses were conducted by using hierarchical multiple regression models. Multiple linear regression modelling was used in order to understand how much variability in mental health indicators could be explained by the explored predictors. There was one regression analysis for emotional well-being, as the dependent variable, and one for depressive symptoms. Each of the two analyses consisted of three steps. In the first step only the control variables (gender, perceived social support and self-regulation) were used as model predictors. In the following steps, the variables of interest were included in the model: perceived stress in the second step and resilience in the third step. Bootstrapping method in Hayes’ PROCESS tool was used to test the indirect effects of perceived stress on mental health indicators through resilience. In order to explore whether the indirect effect was moderated by social support. This variable was split into two categories and separate indirect effects were explored in each respective part of the data. This procedure examined whether there was evidence of indirect effect under both the lower and the higher social support condition. Results: In the final model for EWB only two variables were found to be statistically significant and the total variance explained by the model as a whole was 36%. These were perceived stress, which was negatively associated with EWB (β=-0,393, p‹0.001), and resilience, which was positively associated with EWB (β=0,288, p‹0.001). A partial indirect effect of perceived stress on EWB through resilience was found, b = -0.105, BCa CI (-0.216, - 0.037). The total variance explained by the final model for M-BDI was 49% and the results revealed that in this final model, perceived stress (β=0.421, p‹0.001) was positively associated with M-BDI, and resilience (β=- 0.240, p‹0.01) and social support (β=-0.155, p‹0.01) were negatively associated with M-BDI. A partial indirect effect of perceived stress on M-BDI through resilience was found, b = 0.232, BCa CI (0.039, 0.573). This indirect effect was moderated by social support which meant that the indirect effect depends on the level of social support. The indirect effect of perceived stress on the M-BDI through resilience was found but only amongst students with a lower level of social support (b = 0.545, BCa CI 0.113, 1.224). Conclusion: The results have shown that the strongest hypothesized contributions of perceived stress and resilience to the variance of both the explored health indicators were confirmed in this study. It was further found that gender, social support and self-regulation did not contribute significantly to the variance of the final regression model for EWB. In addition, self-regulation did not contribute significantly to the variance of the final regression model for the M-BDI. This study found a partial indirect effect of perceived stress on EWB through resilience which was found after checking for gender, social support and self-regulation. A partial indirect effect of perceived stress on the M-BDI through resilience was found; but only among students with a lower level of social support after checking for gender and self-regulation. This study extends previous research-based knowledge regarding the relationship between perceived stress, resilience and mental health indicators by using a comprehensive model to predict health indicators as well as through the exploration of the indirect effect that perceived stress has on mental health indicators. These findings suggest that students with a higher level of stress perception and lower level of resilience as well as lower social support, were exposed to the risk of depressive symptoms development. This supports the importance of resilience enhancing especially among students with lower levels of social support under stressful life conditions. This study contributes to the understanding of the underlying mechanisms of perceived stress and mental health by exploring the role of resilience and corroborates the importance of social support and resilience-based intervention. The main limitations of the present study were that all the data were obtained via self-report measures and through online data collection.

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