Emotional processing is not enough: Relations among resilience, emotional approach coping, and posttraumatic stress symptoms among combat Veterans

Abstract: Combat soldiers are exposed to various potentially traumatic events and face high risk of developing military-related psychopathology, such as depression, posttraumatic stress and grief (PTSS). However, a strong body of research shows that resilience is the default in the aftermath of trauma and indeed, many veterans do not develop high symptomatic levels. To explicate this inconsistency, the current study examined the associations among PTSS, resilience, and patterns of emotional-approach coping. A sample of 595 male combat veterans filled out questionnaires on trauma exposure, PTSS, depressive symptoms, resilience, and emotional-approach coping. Their data were analyzed using structural equation modeling path analysis. Participants reported high exposure to potentially traumatic events during service. Mean scores were high for resilience and relatively low for PTSS and depressive symptoms; 13% had a clinical level of posttraumatic stress disorder. Structural equation modeling revealed that emotional-approach coping strategies mediated the relationship between resilience and PTSS. However, emotional expression was associated with lower PTSS levels, whereas emotional processing was associated with higher PTSS levels. These results suggest that although emotional-approach coping was related to higher resilience, emotional expression (an intrapersonal coping strategy) might have a more positive effect than self-oriented emotional coping strategies. Providing veterans with supportive opportunities and a wider repertoire of emotional coping skills might enhance their well-being, reduce postservice emotional distress while not harming veterans' resilience levels.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.