Objective: To investigate the interaction between mental elasticityand occupational stress and depressionin female nurses and the mediating effect of mental elasticity, as well as the functioning way of mental elasticity in occupational stress-depression. Methods: From August to October, 2015, cluster sampling was used to select 122 female nurses in a county-level medical institution as study subjects. The Connor-Davidson Resilience Scale (CD-RISC) , Occupational Stress Inventory-Revised Edition (OSI-R) , and Self-Rating Depression Scale (SDS) were used to collect the data on mental elasticity, occupational stress, and depression and analyze their correlation and mediating effect. Results: The 122 female nurses had a mean mental elasticity score of 62.4±15.1, which was significantly lower than the Chinese norm (65.4±13.9) (P<0.05) ; the mean depression score was 41.0±7.7, which was significantly higher than the Chinese norm (33.5±8.6) (P<0.01) , and the incidence rate of depression of 52.5%. Mental elasticity was negatively correlated with occupational stress and depression (r=-0.559 and -0.559, both P<0.01) . Occupational stress and the two subscales mental stress reaction and physical stress reaction were positively correlated with depression (r=0.774, 0.734, and 0.725, all P<0.01) . After adjustment for confounding factors, occupational stress had a positive predictive effect on depression (β=0.744, P<0.01) , and mental elasticity had a negative predictive effect on depression (β=-0.221, P<0.01) . The analysis of mediating effect showed a significant direct effect of occupational stress on depression and a significant mediating effect of mental elasticity (a=-0.527, b=-0.227, c=0.744, c’=0.627; all P<0.01) , and the mediating effect of mental elasticity accounted for 16.08% of the total effect. Conclusion As a partial mediating variable, mental elasticity has an indirect effect on the relationship between occupational stress and depression and can alleviate the adverse effect of occupational stress and reduce the development of depression.

Objective:To explore the risk factors of insomnia among employees in the thermal power generation industry and the network relationships between their interactions, and to provide scientific basis for personalized interventions for high-risk groups with insomnia.Methods:In November 2022, 860 employees of a typical thermal power generation enterprise were selected as the research subjects by cluster sampling. On-site occupational health field surveys and questionnaire surveys were used to collect basic information, occupational characteristics, anxiety, depression, stress, occupational stress, and insomnia. The interaction between insomnia and occupational health psychological factors was evaluated by using structural equation model analysis and Bayesian network construction.Results:The detection rates of anxiety, depression and stress were 34.0% (292/860), 32.1% (276/860) and 18.0% (155/860), respectively. The total score of occupational stress was (445.3±49.9) points, and 160 workers (18.6%) were suspected of insomnia, and 578 workers (67.2%) had insomnia. Structural equation model analysis showed that occupational stress had a significant effect on the occurrence of insomnia in thermal power generation workers (standardized load coefficient was 0.644), and occupational health psychology had a low effect on insomnia (standardized load coefficient was 0.065). However, the Bayesian network model further analysis found that anxiety and stress were the two parent nodes of insomnia, with direct causal relationships, the arc strength was-8.607 and -15.665, respectively. The model prediction results showed that the probability of insomnia occurring was predicted to be 0 in the cases of no stress and anxiety, low stress without anxiety, and no stress with low anxiety. When high stress with low anxiety and low stress with high anxiety occurred, the predicted probability of insomnia occurring were 0.38 and 0.47, respectively. When both high stress and high anxiety occurred simultaneously, the predicted probability of insomnia occurring was 0.51.Conclusion:Bayesian network risk assessment can intuitively reveal and predict the insomnia risk of thermal power generation workers and the network interaction relationship between the risks. Anxiety and stress are the direct causal risks of insomnia, and stress is the main risk of individual insomnia of thermal power generation workers. The occurrence of insomnia can be reduced based on scientific intervention of stress conditions.

Objective:To explore the risk factors of insomnia among employees in the thermal power generation industry and the network relationships between their interactions, and to provide scientific basis for personalized interventions for high-risk groups with insomnia.Methods:In November 2022, 860 employees of a typical thermal power generation enterprise were selected as the research subjects by cluster sampling. On-site occupational health field surveys and questionnaire surveys were used to collect basic information, occupational characteristics, anxiety, depression, stress, occupational stress, and insomnia. The interaction between insomnia and occupational health psychological factors was evaluated by using structural equation model analysis and Bayesian network construction.Results:The detection rates of anxiety, depression and stress were 34.0% (292/860), 32.1% (276/860) and 18.0% (155/860), respectively. The total score of occupational stress was (445.3±49.9) points, and 160 workers (18.6%) were suspected of insomnia, and 578 workers (67.2%) had insomnia. Structural equation model analysis showed that occupational stress had a significant effect on the occurrence of insomnia in thermal power generation workers (standardized load coefficient was 0.644), and occupational health psychology had a low effect on insomnia (standardized load coefficient was 0.065). However, the Bayesian network model further analysis found that anxiety and stress were the two parent nodes of insomnia, with direct causal relationships, the arc strength was-8.607 and -15.665, respectively. The model prediction results showed that the probability of insomnia occurring was predicted to be 0 in the cases of no stress and anxiety, low stress without anxiety, and no stress with low anxiety. When high stress with low anxiety and low stress with high anxiety occurred, the predicted probability of insomnia occurring were 0.38 and 0.47, respectively. When both high stress and high anxiety occurred simultaneously, the predicted probability of insomnia occurring was 0.51.Conclusion:Bayesian network risk assessment can intuitively reveal and predict the insomnia risk of thermal power generation workers and the network interaction relationship between the risks. Anxiety and stress are the direct causal risks of insomnia, and stress is the main risk of individual insomnia of thermal power generation workers. The occurrence of insomnia can be reduced based on scientific intervention of stress conditions.