Aims: The main purpose of the present study was to extend the Job Demand Control Support (JDCS) model analyzing the direct and interactive role of occupational coping self-efficacy (OCSE) beliefs. Background: OCSE concern an individual's beliefs about one's ability to cope with occupational stressors. The interplay between occupational stressors, job resources and self-efficacy beliefs is poorly investigated. The present research attempts to address this gap. Design: Cross-sectional survey. Method: Questionnaire data from 1479 nurses (65% response) were analyzed. Hierarchical regression analyses were used to test the direct and moderating role of OCSE in conjunction with job demands (i.e., time pressure), and two job resources: job control (i.e., decision latitude and skill discretion) and social support (i.e., supervisor support and coworker support) in predicting psychological distress and well-being. Results: Our findings indicated that high demands, low job control and low social support additively predicted the distress/well-being outcomes (job satisfaction, emotional exhaustion, depersonalization, psychological distress, and somatic complaints). Beyond the main effects, no significant interactive effects of demands, control, and support were found. Occupational coping self-efficacy (OCSE) accounted for an additional 1% to 4% of the variance in the outcomes, after controlling for the JDCS variables. In addition, the results indicate that occupational coping self-efficacy buffers the association between low job control and the distress dimensions emotional exhaustion, depersonalization, and psychological distress. Low control was detrimental only for nurses with low occupational coping self-efficacy. Conclusion: Our results suggest expanding the JDCS model incorporating individual characteristics such as occupational coping self-efficacy beliefs, for predicting psychological distress and well-being. Limitations of the study and practical implications are discu

Low life satisfaction has often been associated with problematic social media use (PSMU), problematic smartphone use (PSU), FoMO and psychological distress. However, no studies have analyzed the relationship between life satisfaction, PSMU, PSU, FoMO, and psychological distress, in an integrated model. The present study hypothesized that life satisfaction may influence PSMU and PSU through the role of FoMO and psychological distress. A cross-sectional survey completed by 537 Italians (82.9% females [ n = 445] and 17.1% males [ n = 92], mean age = 35.35 years [SD ± 12.14]), included the Satisfaction With Life Scale, Bergen Social Media Addiction Scale, Smartphone Application-Based Addiction Scale, Depression Anxiety and Stress Scale, and Fear of Missing Out Scale. The results indicated direct negative associations between life satisfaction and both PSMU and PSU. Additionally, the findings indicated that both FoMO and psychological distress acted as full mediators in this complex relationship, suggesting that problematic technology use may be driven by emotional vulnerabilities such as fear of social exclusion and heightened levels of psychological distress. The present study contributes to understanding the psychological mechanisms underlying the relationship between life satisfaction, fear of missing out, psychological distress, and problematic technology use, offering insights for potential interventions aimed at reducing the negative impact of technology on well-being.

The main aim of this longitudinal study was to test the Job Demand-Control-Support (JDCS) model and to analyze whether changes in psychosocial job characteristics are related to (changes in) burnout. Previous studies on the effects of JDCS variables on burnout dimensions have indicated that the iso strain hypothesis (i.e., high job demands, low control and low support additively predict high stress reactions) and the buffer hypotheses (i.e., high job control and/or social support is expected to moderate the negative impact of high demands on stress reactions) have hardly been examined concurrently in a longitudinal design; and that the effects of changes of psychosocial job variables on burnout dimensions have hardly been analysed. This two wave study was carried out over a period of 14 months in a sample of 217 Italian nurses. Method: Hierarchical regression analyses were used to test the cross lagged main and interactive effects of JDCS variables, and to analyse the across-time effects of changes in JDCS dimensions on burnout variables. The Time 1 job characteristics explained 2 to 8% of the variance in the Time 2 burnout dimensions, but no support for the additive or the buffer hypothesis of the JDCS model was found. Changes in job characteristics explained an additional 3 to 20% of variance in the Time 2 burnout dimensions. Specifically, high levels of emotional exhaustion at Time 2 were explained by high levels of social support at Time 1, and unfavourable changes in demands, control and support over time; high depersonalisation at Time 2 was explained by high social support at time 1 and by an increase in demands over time; and high personal accomplishment at Time 2 was predicted by high demands, high control, interactive effect demands X control X social support, at Time 1, and by a decrease in demands over time. No reversed effects of burnout on work characteristics have been found. Our findings suggest that the work environment is subject to changes: the majority of employees experienced considerable changes in all job conditions over time. These changes impacted employee burnout. Limitations and implications of the study are discussed.