Pathway from childhood trauma to nonsuicidal self-injury in adolescents with major depressive disorder: the chain-mediated role of psychological resilience and depressive severity.

This study aimed to explore the pathway from childhood trauma to nonsuicidal self-injury (NSSI) in adolescents with major depressive disorder (MDD) and to examine the chain-mediating role of psychological resilience and depressive symptoms in this pathway. A total of 391 adolescents with MDD were recruited in the present study. The Chinese version of the Childhood Trauma Questionnaire-Short Form (CTQ-SF), the Chinese version of the Symptoms Check List-90 (SCL-90), the Chinese version of the Conner-Davidson Resilience Scale (CD-RISC), and the Ottawa Self-Injury Inventory Chinese Revised Edition (OSIC) were used to evaluate childhood trauma, depressive symptoms, psychological resilience and NSSI, respectively. Our results showed that 60.87% of adolescents with MDD had NSSI in the past month. Childhood trauma frequency was negatively correlated with psychological resilience but positively correlated with depressive symptoms and NSSI severity in adolescents with MDD. The stepwise logistic regression analysis identified that age, childhood trauma and depressive symptoms could independently predict the occurrence of NSSI, and the three-step hierarchical regression showed that childhood trauma, psychological resilience and depressive symptoms were all significantly associated with NSSI frequency in adolescents with MDD. Furthermore, the chain-mediation analysis revealed that psychological resilience and depression serially mediated the relationship between childhood trauma and NSSI in adolescents with MDD. Interventions targeted at improving resilience and depression may mitigate the impact of childhood trauma severity on NSSI risk in adolescents with MDD.

Mitigating the impact of outliers in traffic crash analysis: A robust Bayesian regression approach with application to tunnel crash data.

Traffic crash datasets are often marred by the presence of anomalous data points, commonly referred to as outliers. These outliers can have a profound impact on the results obtained through the application of traditional methods such as logit and probit models, commonly used in the domain of traffic safety analysis, resulting in biased and unreliable estimates. To mitigate this issue, this study introduces a robust Bayesian regression approach, the robit model, which utilizes a heavy-tailed Student's t distribution to replace the link function of these thin-tailed distributions, effectively reducing the influence of outliers on the analysis. Furthermore, a sandwich algorithm based on data augmentation is proposed to enhance the estimation efficiency of posteriors. The proposed model is rigorously tested using a dataset of tunnel crashes, and the results demonstrate its efficiency, robustness, and superior performance compared to traditional methods. The study also reveals that several factors such as night and speeding have a significant impact on the injury severity of tunnel crashes. This research provides a comprehensive understanding of the outliers treatment methods in traffic safety studies and offers valuable recommendations for the development of appropriate countermeasures to effectively prevent severe injuries in tunnel crashes.