Evaluation of mental health first aid training for family members of military veterans with a mental health condition
Abstract: Background: A concerning proportion of former Australian Defence Force (ADF) members meet criteria for a mental health condition. Mental health difficulties not only affect the individual veteran. They have been found to negatively impact the mental health of family, with an increased likelihood for family members of veterans developing a mental health condition. The aim of this study was to evaluate whether participating in a Mental Health First Aid (MHFA) program improved family members of veterans mental health knowledge, reduced personal and perceived mental health stigma, reduced social distancing attitudes and increased confidence and willingness to engage in MHFA helping behaviours. Additionally, the study measured participant’s general mental health and levels of burnout. Method: The study utilised an uncontrolled design with assessment at three time points (baseline, post program and three-month follow-up). Participants (N = 57) were immediate and extended family members of former ADF members with a mental health condition, who took part in a two-day standard adult MHFA program. At each time point, participants completed self-report measures assessing mental health knowledge, personal and perceived mental health stigma, social distancing attitudes, confidence and willingness to engage in MHFA helping behaviours, general mental health and burnout. Cochranes Q and repeated measures ANOVA was computed to measure the impact of time on the outcome variables. Results: Results indicated significant improvements in MHFA knowledge and confidence in providing MHFA assistance. Significant reductions in personal mental health stigma (i.e. an individual’s attitude towards mental health) for schizophrenia were observed and maintained at follow up. High levels of perceived mental health stigma (i.e. the belief an individual holds about others attitudes towards mental health) were reported with no significant changes observed following the MHFA program. Results did not indicate any significant benefit in improving general psychological distress or burnout at follow up. The participant sample had high levels of mental health difficulties with over half reporting a lifetime mental health diagnosis.
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.