Caregiver experiences of public services following child trauma exposure: a qualitative study
Abstract: Background: Many children in low and middle income countries (LMIC) are exposed to trauma. Contact with public services are a potential influence on parent–child reactions and coping post-trauma. Little is known about how caregivers perceive these interactions. Methods: The aim of this study was to explore caregivers’ experiences of accessing and interacting with public services post-trauma and perceptions of needed improvements to public services in a LMIC context. Qualitative interviews were conducted with 20 female caregivers from a high-risk settlement in South Africa after child trauma exposure. Results: Three themes and seven sub-themes were identified regarding caregivers’ perceptions of interactions with public services post-trauma. The key themes identified related to (1) communication and exchanges with law enforcement, (2) consequences of an under-resourced justice system and (3) importance of communication and empathy in the healthcare system. Interactions with police were often positive. However, caregivers explained that police-family communication post-trauma could be improved and may help to lessen caregiver anxiety and concerns for the child’s safety post-trauma. Caregivers perceived the judicial system to be under-resourced as contact with the judicial system was often protracted and caused child anxiety and distress. Medical treatment was reportedly rushed, with extensive waiting times and little information provided to caregivers regarding the child’s injuries or treatment. Some medical staff were perceived as unsympathetic during the child’s treatment which was found to exacerbate caregiver and child distress post-trauma. Conclusions: This study provides insight into caregiver experiences of accessing public services following child trauma exposure in a high-risk LMIC context. Public services were perceived as oversubscribed and under-resourced and negative interactions often influenced caregiver responses and appraisals of child safety. Given the impact of poor interactions with public services on families post-trauma, additional research is needed to investigate feasible improvements to public services in LMIC.
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.