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Effects of social support and resilient coping on violent behavior in military veterans.

Elizabeth Van VoorheesH Ryan WagnerJean C BeckhamDaniel W BradfordLydia C NealWalter E PenkEric B Elbogen
Published in: Psychological services (2018)
Violence toward others has been identified as a serious postdeployment adjustment problem in a subset of Iraq- and Afghanistan-era veterans. In the current study, we examined the intricate links between posttraumatic stress disorder (PTSD), commonly cited psychosocial risk and protective factors, and violent behavior using a national randomly selected longitudinal sample of Iraq- and Afghanistan-era United States veterans. A total of 1,090 veterans from the 50 United States and all United States military branches completed 2 waves of self-report survey-data collection 1 year apart (retention rate = 79%). History of severe violent behavior at Wave 1 was the most substantial predictor of subsequent violence. In bivariate analyses, high correlations were observed among risk and protective factors, and between risk and protective factors and severe violence at both time points. In multivariate analyses, baseline violence (OR = 12.43, p < .001), baseline alcohol misuse (OR = 1.06, p < .05), increases in PTSD symptoms between Waves 1 and 2 (OR = 1.01, p < .05), and decreases in social support between Waves 1 and 2 (OR = .83, p < .05) were associated with increased risk for violence at Wave 2. Our findings suggest that rather than focusing specifically on PTSD symptoms, alcohol use, resilience, or social support in isolation, it may be more useful to consider how these risk and protective factors work in combination to convey how military personnel and veterans are managing the transition from wartime military service to civilian life, and at what point it might be most effective to intervene. (PsycINFO Database Record
Keyphrases
  • social support
  • posttraumatic stress disorder
  • depressive symptoms
  • mental health
  • healthcare
  • emergency department
  • sleep quality
  • climate change
  • machine learning