Attitudes of Iraq and Afghanistan War Veterans toward Gay and Lesbian Service Members
Abstract: U.S. policy banning openly gay and lesbian personnel from serving in its military rests on the belief that heterosexual discomfort with lesbian and gay service members in an integrated environment would degrade unit cohesion and readiness. To inform this policy, data from a 2006 survey of Iraq and Afghanistan war veterans are analyzed in this study. Views of these war veterans are consistent with prior surveys of military personnel showing declining support for the policy: from about 75 percent in 1993 to 40 percent in this survey. Among the demographic and military experience variables analyzed, comfort level with lesbian and gay people was the strongest correlate of attitudes toward the ban. War veterans indicated that the strongest argument against the ban is that sexual orientation is unrelated to job performance and that the strongest argument in favor of the ban is a projected negative impact on unit cohesion. However, analyses of these war veterans’ ratings of unit cohesion and readiness revealed that knowing a gay or lesbian unit member is not uniquely associated with cohesion or readiness; instead, the quality of leaders, the quality of equipment, and the quality of training are the critical factors associated with unit cohesion and readiness.
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.