A qualitative study of the capabilities of family members of Veterans living with operational stress injuries

Abstract: Objective: This qualitative study explores the experiences of 26 family members supporting Canadian Armed Forces veterans living with mental health problems including operational stress injuries (OSIs). Background: The research focusing on military‐connected families emphasizes the impacts of service on the families. Little is known about how families impact military veterans, particularly those diagnosed with OSIs. This research addresses that gap by revealing the capabilities and demands characterizing their everyday lives. Method: Semistructured interviews with 26 individuals identifying as family members of Canadian Armed Forces (CAF) veterans and three focus groups (three participants in each group for a total of nine) with family members of CAF veterans released within the preceding 10 years were conducted. The veterans were living with broadly defined diagnosed and undiagnosed mental health problems including OSIs. Using the family adjustment and adaptation (FAAR) model as an organizational framework, demands and capabilities embodied within the everyday lives of the family members were revealed. Results: Monitoring the well‐being of the veteran, managing daily life, accessing and mobilizing resources, and caregiving were discussed as capabilities by participants in this study. These capabilities buffer the demands associated with the veteran's mental health problems. Conclusions: Results of this study endorse recommendations for family‐centered program and service development, modeled on approaches that recognize the systemic and relational contexts instrumental in supporting positive outcomes for veterans with OSIs. Implications: Further research exploring the complex, interdependent, and interactional role of families supporting veterans with OSIs is warranted.

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.