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Posttraumatic Stress Disorder Symptoms and Reckless Behaviors: A Network Analysis Approach
Authors:Cherie Armour  Talya Greene  Ateka A. Contractor  Nicole Weiss  Katherine Dixon-Gordon  Jana Ross
Affiliation:1. School of Psychology, Queen's University Belfast, Northern Ireland, United Kingdom;2. Department of Community Mental Health, University of Haifa, Haifa, Israel;3. Department of Psychology, University of North Texas, Denton, Texas, USA;4. Department of Psychology, University of Rhode Island, Kingston, Rhode Island, USA;5. Department of Psychological and Brain Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USA
Abstract:Existing literature indicates a theoretical and empirical relation between engagement in reckless behaviors and posttraumatic stress disorder (PTSD). Thus, the DSM-5 revision of the PTSD nosology added a new “reckless or self-destructive behavior” (RSDB) symptom (Criterion E2). The current study applied a network analytic approach to examine the item-level relations among a range of reckless behaviors and PTSD symptom clusters. Participants were recruited from Amazon's Mechanical Turk (N = 417), and network analysis was conducted with 20 variables: six PTSD symptom clusters, corresponding to the hybrid model of PTSD (Armour et al., 2015) and excluding the externalizing behavior cluster (Community 1), and 14 items related to reckless behavior (Community 2). The results showed that the network associations were strongest within each construct (i.e., within PTSD and within reckless behaviors), although several bridge connections (i.e., between PTSD clusters and reckless behaviors) were identified. Most reckless behavior items had direct associations with one or more PTSD symptom clusters. The present findings support the existence of close relations between a variety of reckless behaviors and PTSD symptom clusters beyond their relations with DSM Criterion E2. The results provide testable hypotheses about the associations between specific reckless behaviors and PTSD symptom clusters, which may inform future research.
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