Accessing and sustaining work after Service: the role of Active Labour Market Policies (ALMP) and implications for HRM
Abstract: This article considers the extent to which Active Labour Market Policies (ALMPs) support the sustained inclusion of veterans in the civilian labour market. Drawing on the first in-depth research into veteran’s interactions with the UK’s Public Employment Services (PES) and other contracted providers, we present analysis of qualitative longitudinal data from 68 veterans. We demonstrate the important role ALMPs play in mediating the employment relationship, showing how veterans claiming out-of-work benefits are typically either ‘pushed’ towards inappropriate jobs or ‘parked’ through their exclusion from employment support when deemed unfit for work. This not only exposes veterans and other jobseekers to poor quality work but undermines both job match and inclusive employment practices. Furthermore, the potential for more positive outcomes through engagement with employers and HRM practitioners is not being realised. This is significant for veterans in the UK and beyond, where policymakers make broader commitments to post-Service integration into civilian employment. We critique Work First approaches centred on those deemed work ready and contribute to broader theorisation around interactions between the state and HRM, arguing the need for pluralist approaches which incorporate ALMPs.
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.