Correlates of Bystander Intervention Attitudes and Intentions Among Young Adult Active Duty Male Soldiers
Abstract: Sexual assault is a significant problem within the United States military. Bystander intervention skills training is recognized as a promising strategy for sexual assault prevention within both civilian and military populations. Sexual assault prevention programs which include training in bystander intervention teach individuals to notice situations that may pose a risk for harm and safely act to positively influence the outcome. This study examines correlates of bystander intervention attitudes and intentions among young adult active duty male soldiers (N = 282) between the ages of 18 and 24. Positive bystander intervention attitudes and intentions were associated with lower levels of rape myth acceptance, greater discomfort with sexism, lower likelihood of continuing an unwanted sexual advance after verbal resistance from a partner, greater likelihood of gaining verbal consent from a partner, and greater perceived peer approval for bystander intervention. In a multiple regression, perceived peer approval for bystander intervention and self-reported lower likelihood of continuing a sexual advance after verbal resistance from a partner emerged as significant predictors of positive bystander intervention attitudes and intentions (R2 =.41). Given that perceptions of peer norms are modifiable, these findings highlight the importance of addressing peer norms in bystander intervention training programs for military personnel.
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.