Veteran Help-Seeking Behaviour for Mental Health Issues: a Systematic Review
Abstract: Serving military personnel and veterans have been identified to have a high prevalence of mental health disorders. Despite this, only a significantly small number seek mental healthcare. With the UK beginning to invest further support to the armed forces community, identification of barriers and facilitators of help-seeking behaviour is needed. Corresponding literature search was conducted in PsycINFO, PsycArticles, Medline, Web of Science and EBSCO. Articles which discussed barriers and facilitators of seeking help for mental health concerns in the veteran population were included. Those which discussed serving personnel or physical problems were not included within this review. A total of 26 papers were analysed. A number of barriers and facilitators of help-seeking for a mental health issue within the veteran population were identified. Barriers included stigma, military culture of stoicism and self-reliance, as well as deployment characteristics of combat exposure and different warzone deployments. Health service difficulties such as access and lack of understanding by civilian staff were also identified. Facilitators to help combat these barriers included a campaign to dispel the stigma, including involvement of veterans and training of military personnel, as well as more accessibility and understanding from healthcare staff. While some barriers and facilitators have been identified, much of this research has been conducted within the USA and on male veterans and lacks longitudinal evidence. Further research is needed within the context of other nations and female veterans and to further indicate the facilitators of help-seeking among veterans.
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.