Workplace perceptions based upon disability and veteran identities
Abstract: We investigated the intersectionality of disability and veteran status to determine whether workplace perceptions were impacted by diverse identity experiences. Our sample was 16,000 U.S. Department of Veterans Affairs (VA) employees who reported having a disability, were veterans, or both. We also included a subset of nondisabled, nonveteran staff for comparative purposes. The data source was the 2021 VA All Employee Survey, an annual, confidential, voluntary organizational-satisfaction census within the VA. Using a mixed-method approach, we found that disabled, nonveteran employees reported greatest dissatisfaction with the workplace, particularly around feelings of disrespect from colleagues. Individuals with disabilities (both veterans and nonveterans) reported higher levels of burnout than those without disabilities. Finally, veterans were more concerned about accountability of staff and leaders compared to nonveterans. We discuss results in an applied context, suggesting how they can inform organizational efforts for diversity, equity, and inclusion. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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.