A Great Place to Start? The Effect of Prior Military Service on Hiring
Abstract: This article examines the effect of prior military service on hiring for entry-level jobs in a major metropolitan labor market. The research employs an audit method in which resumes differing only in the presentation of military experience versus civilian work experience are faxed in response to an advertised position. Results suggest that employers exhibit preferential treatment of black military veterans with transferable skills over black nonveterans. Veterans with traditional military experience in the combat arms do not experience preferential treatment by employers, regardless of racial/ethnic background. These findings suggest a possible mechanism generating the postmilitary employment benefit among blacks found in prior observational studies. A veteran premium in hiring may stem from the concentration of blacks in military occupational specialties with a high degree of civilian transferability, combined with employer preferences for military veterans with such work experience over their nonveteran peers.
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.