Emotion socialization profiles in military parents: associations with post‐traumatic stress disorder
Abstract: Military families face many difficulties, including a parent deploying to a warzone and the subsequent risk of returning with symptoms of posttraumatic stress disorder (PTSD). Symptoms of PTSD are associated with parenting difficulties; however, little is known about how PTSD symptoms may be associated with emotion socialization (ES), a set of processes crucial to children's emotional well-being. This project investigated observed ES behaviours in deployed and non-deployed parents in a sample of 224 predominantly White, non-Hispanic National Guard/Reserve (NG/R) families with deployed fathers, non-deployed mothers, and a child between the ages of 4 and 13. Parents completed self-report questionnaires and families engaged in videotaped parent–child discussions, which were coded for three types of ES behaviours. Latent profile analyses of the coded behaviours identified five profiles of parental ES: Balanced/Supportive, Balanced/Limited Expression, Unsupportive/Distressed, Unsupportive/Positive, and Involved/Emotive/Angry. Multinomial logistic regressions of each parent's profile membership on fathers' PTSD symptoms revealed no significant associations, while additional analyses including additional family factors revealed that greater father PTSD symptoms were associated with a greater likelihood of mothers being in the Balanced/Supportive profile compared to the Balanced/Limited Expression profile, particularly when children displayed average to low levels of emotion during discussion tasks. No other significant associations with PTSD symptoms were detected. Overall, in contrast to the hypotheses, the majority of these findings indicated that PTSD symptoms did not play a significant role in parental ES behaviours.
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.