Development and psychometric evaluation of the mental readiness for military transition scale (MT-Ready)
Abstract: The transition to civilian life following separation from military service is associated with increased risk of mental health disorders, suicide, and poor adjustment. No measure currently enables pre-separation screening to assess mental readiness for transition and identify personnel most at risk of poor outcomes. The Mental Readiness for Military Transition Scale (MT-Ready) was developed to identify psychosocial factors predictive of post-separation psychological adjustment and mental health. Phase I was a qualitative study including transitioned veterans (n = 60), partners of transitioned veterans (n = 20) and mental health clinicians (n = 20) which enabled development of candidate items that were subsequently piloted with a current serving Australian Defence Force (ADF) sample (n = 19). Phase II included evaluation of the factor structure, psychometric properties, and scale refinement of the initial pool of 50 items with a convenience sample of transitioning ADF personnel (n = 345). Analyses included exploratory factor analysis, evaluation of test-retest reliability, internal consistency, convergent, divergent, discriminant and predictive validity. Receiver Operating Characteristic Curve Analysis was also conducted to determine an optimal cut-off score. Exploratory factor analysis resulted in a 15-item, three-factor solution that explained 62.2% of the variance: Future focus and optimism; Anger and perceived failure; Civilian connections and social support. Reliability and convergent, divergent, and discriminant validity was established. Receiver Operating Characteristic Curve Analysis determined a cut-off score of 55. MT-Ready scores significantly differentiated those reporting adjusting versus not adjusting to civilian life 3.7 months post-separation, and predicted post-separation outcomes including symptoms of Posttraumatic Stress Disorder, depression, anxiety, psychological adjustment and quality of life. This evaluation provides promising evidence the MT-Ready is a valid, reliable measure of mental readiness for transition, with predictive capability and considerable potential to assist prevention of poor post-separation outcomes among military personnel.
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.