Development of the complex trauma screener: A brief measure of ICD-11 PTSD and complex PTSD

Abstract: The purpose of this study was to develop the Complex Trauma Screener (CTS), a brief screener (seven items) of the ICD-11 trauma disorders that can be used in "quick-paced" facilities. We examined the factor structure of the CTS in two separate samples: civilian college students (N = 823) and military veterans (N = 130) who reported exposure to at least one traumatic event. Confirmatory factor analyses (CFAs) supported two highly-correlated factors (post-traumatic stress disorder [PTSD] and Disturbances in Self-Organization [DSO]) that loaded on the ICD-11-consistent items. The model fit indices indicated good to excellent model fit in both samples, and the internal consistencies for the scales were borderline to good (α = 0.68-0.86). Supplementary analyses supported the gender invariance of the CFA model in the civilian student sample, as well as convergent (with another trauma inventory) and discriminant validity (with borderline disorder features, depression, and mania) of the CTS in both samples. The CTS is, to our knowledge, the shortest instrument designed to measure the ICD-11 trauma disorders and is ideal for "fast-paced" facilities that have significant assessment time restraints. The CTS is, therefore, is a psychometrically-validated instrument that can help mental health professionals efficiently screen adults for ICD-11 trauma disorders.

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.