Systematic improvements to the army's deployment cycle resilience training using a comprehensive, iterative process

Abstract: INTRODUCTION: To support soldier readiness and mitigate the mental health consequences of deployments, Army regulation mandates soldiers to receive Deployment Cycle Resilience Training (DCRT) throughout their deployment cycle. A recent evaluation revealed several issues with the existing version that threatened the relevancy and usefulness of the training. The present article details the systematic approach taken by the Research Transition Office at the Walter Reed Army Institute of Research to revise the DCRT curriculum and presents the revision updates that are now included in DCRT version 3. METHOD: Curriculum developers (n = 2) with subject matter expertise relevant to the project followed an iterative process that was critical to the efficacy of the revisions. Developers used the existing DCRT modules as the curriculum framework and utilized several materials to inform the revisions to include Army doctrine, data from the quality improvement evaluation conducted by the Walter Reed Army Institute of Research, and the current research related to the deployment cycle, resilience, and behavior change. Internal and external stakeholders (n = 31) provided iterative feedback to ensure each of the six modules met DCRT revision objectives. RESULTS: The revised DCRT curriculum was implemented in August 2021. The resulting revisions included an increase in inclusivity, an emphasis on growth opportunities, an integrative approach to the deployment cycle phases, and greater practical application. Additionally, the curriculum incorporates best practices found to enhance the delivery of resilience-based psychoeducational interventions, specifically within high-risk occupational settings like the military. CONCLUSIONS: The revisions outlined in this article enhance the training quality and potential effectiveness of DCRT, which can positively influence soldier and family readiness and mission success. Furthermore, the deliberate and iterative curriculum revision process can serve as a guide to other curriculum development projects, specifically within the military context. Implementation considerations and potential limitations are provided, and future directions are discussed to include the ongoing evaluation.

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