The relationship between corona disease anxiety and sleep disturbances and suicidal ideation in medical staff: The mediating role of resiliency and cognitive flexibility: A cross-sectional study.

Background and Aims: Medical staff have experienced anxiety, sleep disturbances, and suicide due to the COVID-19 epidemic. Thus, this study examined the relationship between corona disease anxiety, sleep problems, and suicidal ideation in medical staff and how resiliency and cognitive flexibility mediate it. Methods: This descriptive-analytical cross-sectional study examined medical staff. In 2022, participants were affiliated with Hamadan University of Medical Sciences, Iran educational and treatment centers. Sampling was done at primary COVID-19 treatment centers. Data was collected using validated instruments. Ethics were observed during data collecting. Results: Path analysis was employed to test hypotheses. Analysis showed significant positive relationships between Corona disease anxiety and sleep disturbances (p = 0.001, ß = 0.438) and suicidal ideation (p = 0.001, ß = 0.310). Conversely, negative and significant associations were identified between resiliency and cognitive flexibility with sleep disturbances and suicidal ideation. Conclusions: The study illustrates how medical staff's psychological health is linked to COVID-19. High Corona disease anxiety causes sleep disturbances and suicidal thoughts. Resilience and cognitive flexibility modulated Corona disease anxiety, sleep problems, and suicidal thoughts. The comprehensive study focuses on medical staff mental health issues, suggesting targeted solutions.

Anticipating influential factors on suicide outcomes through machine learning techniques: Insights from a suicide registration program in western Iran.

Suicide is a global public health concern, with increasing rates observed in various regions, including Iran. This study focuses on the province of Hamadan, Iran, where suicide rates have been on the rise. The research aims to predict factors influencing suicide outcomes by leveraging machine learning techniques on the Hamadan Suicide Registry Program data collected from 2016 to 2017. The study employs Naïve Bayes and Random Forest algorithms, comparing their performance to logistic regression. Results highlight the superiority of the Random Forest model. Based on the variable importance and multiple logistic regression analyses, the most important determinants of suicide outcomes were identified as suicide method, age, and timing of attempts, income, and motivation. The findings emphasize the cultural context's impact on suicide methods and underscore the importance of tailoring prevention programs to address specific risk factors, especially for older individuals. This study contributes valuable insights for suicide prevention efforts in the region, advocating for context-specific interventions and further research to refine predictive models and develop targeted prevention strategies.