In my master's thesis, I aimed to investigate the state of mental health among higher education and research employees at the University of Ljubljana (UL). Within the proposed model, I examined the relationships between job resources, job demands, and mental health outcomes. Specifically, I hypothesized that job resources would negatively predict poor mental health indicators, while job demands would predict them positively. Additionally, I assumed that job resources and demands would be negatively correlated. In an alternative model, I hypothesized that job resources would act as moderators for the relationship between job demands and mental health. I was also interested in the proportion of individuals reaching critical values on poor mental health indicators, determining if there is a profile of employees at the UL reporting more mental health issues, and identifying which aspects of work employees perceive as the most important for their positive and negative mental health. The study included 374 employees at the UL, who answered five main questionnaires about their mental health and perceived work characteristics. The proportion of individuals with clinically significant symptoms of depression, anxiety and burnout was 34.8 %, 29.1 %, and 24.1 %, respectively. Job resources moderately predicted mental health outcomes in a negative direction, while job demands predicted mental health in a low and positive manner. Job resources and demands were moderately negatively correlated, and a low positive correlation was found between autonomy and general job demands. The hypothesized moderating role of job resources was statistically insignificant. Age showed a low negative correlation with depression, anxiety, and burnout scores. The results provide a significant insight into the heterogeneous set of work characteristics that employees at the UL perceive as important for their mental health. The findings of this study can assist in designing interventions to strengthen employees' mental health at the UL and support further research on predicting mental health outcomes with work-specific resources and demands at the UL.
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