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BACKGROUND: Non-suicidal self-injury (NSSI) is a significant public health concern among adolescents with major depressive disorders (MDD). Although previous research has linked child maltreatment (CM) to NSSI, the precise mechanisms remain unclear. This study aims to investigate the association between CM, cognitive reappraisal (CR), negative coping styles (NC) and NSSI in adolescents with MDD, from the perspectives of both Latent Variable Theory and the Network Theory of Mental Disorder. METHODS: A sample of 651 adolescents with MDD was recruited from January to December 2023. Data on CM, CR, NC, and NSSI were collected through paper-based self-reported questionnaires. Data analysis primarily involved structural equation modeling and network analysis. RESULTS: The reporting rate of NSSI among adolescents with MDD was 48.2%. CM showed a significant positive correlation with NSSI. NSSI was affected by CM through three paths: the mediating role of CR, the mediating role of NC, and the chain mediating role of both CR and NC. Emotional abuse (EA) was the central node, while NSSI, EA, and "The urge to cry quietly when faced with troubles"(NC10) were the key bridge nodes. CONCLUSIONS: This study is the first to use both structural equation modeling and network analysis to explore the explore the relationship between CM, CR, NC, and NSSI in adolescents with MDD, providing a theoretical basis for future early prevention and targeted interventions for adolescents with MDD.

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Background: Non-suicidal self-injury (NSSI) is a significant social issue, especially among adolescents with major depressive disorder (MDD). This study aimed to construct a risk prediction model using machine learning (ML) algorithms, such as XGBoost and random forest, to identify interventions for healthcare professionals working with adolescents with MDD. Methods: This study investigated 488 adolescents with MDD. Adolescents was randomly divided into 75% training set and 25% test set to testify the predictive value of risk prediction model. The prediction model was constructed using XGBoost and random forest algorithms. We evaluated the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, recall, F Score of the two models for comparing the performance of the two models. Results: There were 161 (33.00%) participants having NSSI. Compared without NSSI, there were statistically significant differences in gender (P=0.035), age (P=0.036), depressive symptoms (P=0.042), sleep quality (P=0.030), dysfunctional attitudes (P=0.048), childhood trauma (P=0.046), interpersonal problems (P=0.047), psychoticism (P) (P=0.049), neuroticism (N) (P=0.044), punishing and Severe (F2) (P=0.045) and Overly-intervening and Protecting (M2) (P=0.047) with NSSI. The AUC values for random forest and XGBoost were 0.780 and 0.807, respectively. The top five most important risk predictors identified by both machine learning methods were dysfunctional attitude, childhood trauma, depressive symptoms, F2 and M2. Conclusion: The study demonstrates the suitability of prediction models for predicting NSSI behavior in Chinese adolescents with MDD based on ML. This model improves the assessment of NSSI in adolescents with MDD by health care professionals working. This provides a foundation for focused prevention and interventions by health care professionals working with these adolescents.

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Aim: To investigate the current situation and need for post-competence training for psychiatric nurses in China and provide a reference for the development of training programs for psychiatric nurses. Design: A cross-sectional design. Methods: A cross-sectional study was conducted from August to October 2023 with 435 psychiatric nurses from 34 hospitals in 24 provinces of mainland China. A self-administered questionnaire was used for data collection. Descriptive statistics, non-parametric tests, and chi-square tests were used for data analysis. Results: The training content for psychiatric nurses is extensive, and the training load is large. Psychiatric nurses have high training demands for first aid knowledge, emergency handling ability, and anti-riot skills. Nurses with different years of experience have different training needs. The training needs of psychiatric nurses in specialized and general hospitals also different. Conclusion: The training status of psychiatric nurses is not consistent with the demand. Managers should combine this with psychiatric nurses' own work needs to develop practical and effective training programs.

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This study explored the anxiety, depression, and self-efficacy of nurses in Wuhan, China 2 years after the corona virus disease 2019 outbreak. A total of 552 nurses were enrolled in the study. Four well-established test tools were applied: The 9-item patient health questionnaire, The 7-item generalized anxiety disorder, generalized self-efficacy scale, Connor Davidson resilience scale. Twenty-eight points twenty-six percentage of the nurses had mild depression, and 5.62% had moderate or severe depression. Twenty-one points seventy-four percentage of nurses had mild anxiety and 1.82% had moderate or severe anxiety. The average score of self-efficacies is negatively correlated with the average score of the 9-item patient health questionnaire (r = -0.303, P < .01), and the7-item generalized anxiety disorder (r = -0.275, P < .01). The average score of self-efficacies is correlated with the resilience score (R = 0.799, P < .01). Through multiple linear regression analysis, the tenacity dimension and monthly income are most closely related to the sense of self-efficacy. Nurses self-efficacy and resilience are important factors in promoting their psychological well-being. This study suggests that increasing the salary and providing some strategies to increase nurses mental tenacity can promote self-efficacy.

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What is already known about this topic?: The coronavirus disease 2019 (COVID-19) pandemic poses a significant threat to mental health globally and may change the proportion of hospitalized patients. What is added by this report?: This report analyzed and compared the disease characteristics of psychiatric inpatients one year before and after Wuhan lifted lockdown during COVID-19. About 50% of the inpatients were diagnosed with bipolar disorder; females and adolescents had a higher prevalence of mental disorders. What are the implications for public health practice?: More attention should be paid to the mental health of children, adolescents, and females.

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BACKGROUND: The coronavirus disease 2019 (COVID-19) epidemic has broken out and even spread globally. The healthcare system worldwide faces enormous challenges, and nurses are at the highest risk as one of the leading forces. It's worth paying attention to nurses' anxiety and job burnout. This study aimed to investigate nurses' levels of burnout and anxiety during the epidemic of COVID-19 and to analyze influencing factors of burnout. METHODS: A cross-sectional survey was conducted from 19 to 25 February 2020. Questionnaires such as the basic information questionnaire, Maslach Burnout Inventory-General Survey (MBI-GS), and State-Trait Anxiety Inventory (STAI) were used among 1011 nurses in Wuhan tertiary hospitals via the online survey. The final number of valid questionnaires was 885. The effective response rate was 87.5%. RESULTS: The average score of MBI-GS was 11.50, 6.02, 24.47, respectively. The average score for state anxiety was 45.52 and trait anxiety, 43.78. Anxiety was positively associated with emotional exhaustion and cynicism, and negatively related to personal accomplishment. The protective factors of burnout were personnel agency, five years or less work experience, living in hospital dormitory, Wuhan medical team, working time exceeding 9 h, and the best knowledge of COVID-19. The absence of siblings, median job title, working in isolation wards, three or more night shifts per week, living in hotels, and being surrounded by confirmed or suspected medical staff were all negative factors. CONCLUSIONS: Nurses had high anxiety levels during the COVID-19 period, but the level of burnout was mild to moderate. Managers should continue to pay attention to nurses' psychological state and related factors and intervene to stabilize the nursing team.

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BACKGROUND: Health care workers, especially frontline nurses, faced great challenges during the coronavirus disease 2019 (COVID-19) outbreak. AIMS: To assess the magnitude of the psychological status and associated risk factors among nurses in the pandemic center in Wuhan, China. METHODS: In this study, we enrolled nurses from Renmin Hospital of Wuhan University. The questionnaire was designed to obtain basic information of the participants, and included four psychological assessment scales. We issued the questionnaires at two different points of time. We conducted the first survey on January 29 to February 2 (outbreak period) with 709 eligible responses, and the second survey on February 26 to February 28 (stable period) with 621 eligible responses. The nurses from Wuchang Fangcang shelter hospital were also enrolled in the second survey. RESULTS: During the pandemic, over one-third of nurses suffered from depression, anxiety, and insomnia. In the outbreak period, the nurses showed significantly higher risks for depression, anxiety, and posttraumatic stress disorder (PTSD) symptoms than those in the stable period (P < 0.01). Notably, the nurses from the Fangcang shelter hospitals were more likely to present psychological problems than those from other frontline or non-frontline (all P < 0.001) units, especially for insomnia (38.3% with severe insomnia). The nurses from the frontline, with worse physical condition and uncertain concerns about this pandemic as compared to the others, were more likely to bear psychological problems. Thus, online psychological information and sufficient protection conditions were effective interventions to help mitigate psychological distress. The nurses from Fangcang shelter hospitals suffered a significantly higher risk of psychological problems than those from other units. CONCLUSION: The psychological status of nurses needs more attention during the COVID-19 pandemic, especially for those who fought in the frontline during the peak of the outbreak.

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