A schema conceptualisation of psychosocial functioning among transitioned military personnel

Abstract: BackgroundThe military to civilian transition process is often associated with a negative impact on psychosocial functioning. Contemporary approaches to understand this are moving away from focussing on posttraumatic stress disorder (PTSD), to examine the military cultural and environmental impacts of service. Schema theory can provide a useful conceptual framework for understanding these issues. The aim of this study was to explore Early Maladaptive Schemas (EMS) across three samples: transitioned military personnel, veterans and first responders with PTSD, and general adults.MethodThis cross-sectional research used a transitioned military sample recruited specifically for this study (N = 94) and two comparison samples of veterans and first responders diagnosed with PTSD (N = 218), and general adults (N = 264) from previous research. Participants completed a Young Schema Questionnaire (YSQ). Independent t-tests were conducted to compare the three samples.ResultsTransitioned military personnel were significantly higher than the general adults on the EMS of Vulnerability to Harm, Entitlement, Emotional Inhibition, Punitiveness and Unrelenting Standards and lower on the schema of Enmeshment. Transitioned military personnel were significantly lower than the PTSD sample on 11 out of the 18 EMS.ConclusionsThe cluster of EMS evident in the transitioned military sample were conceptualised as 'The Military Mode'. This conceptual framework can be used to understand the psycho-social issues experienced by transitioned military personnel and to inform interventions to promote successful transition.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    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.

    Mental Health Peer reviewed