The First Year After Military Service: Predictors of U.S. Veterans’ Suicidal Ideation

American Journal of Preventive Medicine(2022)

引用 3|浏览2
暂无评分
摘要
INTRODUCTION:Little is known about predictors of military veterans' suicidal ideation as they transition from service to civilian life, a potentially high-risk period that represents a critical time for intervention. This study examined factors associated with veterans' suicidal ideation in the first year after military separation. METHODS:A national sample of U.S. veterans (N=7,383) from The Veterans Metrics Initiative Study reported on their mental health, psychosocial well-being, and demographic/military characteristics in an online survey at 3 and 9 months after separation. Cross-validated random forest models and mean decrease in accuracy values were used to identify key predictors of suicidal ideation. Bivariate ORs were calculated to examine the magnitude and direction of main effects associations between predictors and suicidal ideation. Data were collected in 2016/2017 and analyzed in 2021. RESULTS:In the first year after separation, 15.1% of veterans reported suicidal ideation. Endorsing depression symptoms and, to a lesser extent, identifying oneself as experiencing depression, were most predictive of suicidal ideation. Other psychopathology predictors included higher anxiety and posttraumatic stress disorder symptoms. Psychosocial well-being predictors included higher health satisfaction and functioning, community satisfaction and functioning, and psychological resilience. Logistic models performed similarly to random forest models, suggesting that relationships between predictors and suicidal ideation were better represented as main effects than interactions. CONCLUSIONS:Results highlight the potential value of bolstering key aspects of military veterans' mental health and psychosocial well-being to reduce their risk for suicidal ideation in the first year after separation. Findings can inform interventions aimed at helping veterans acclimate to civilian life.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要