What the public should know about veterans returning from combat deployment to support reintegration: A qualitative analysis
Abstract: Consensus reports have called for interventions to educate civilians about the reintegration challenges that veterans experience. The current study describes veterans’ perspectives of what the public should know and what the public can do to help veterans reintegrate into civilian life. We conducted thematic analysis of written essays from a stratified random sample of 100 US veterans (half women, half deployed from National Guard or Reserves) from Afghanistan and Iraq military operations who had participated in the control writing condition from a randomized controlled trial of expressive writing to improve reintegration outcomes. Veterans described a military-civilian divide that makes reintegration difficult and recommended that the public help bridge this divide. The divide was attributable to the uniqueness of military culture and bonds, the personal changes associated with deployment, and the time it takes for veterans to reacclimate. Five themes captured what the public can do to foster veteran reintegration: understand deployment hardships; appreciate deployment accomplishments; assist veterans in getting professional help; listen, don’t judge; and recognize that employment is critical to reintegration. Themes were present across groupings by gender, type of military service and screening status for posttraumatic stress disorder. Findings can inform interventions that target the public’s understanding of and response to returning veteran. Consistent with an ecological model of reintegration, such interventions have the potential to foster successful reintegration.
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.