Literature Collection

INTRODUCTION: The prevalence of opioid use disorder (OUD) in pregnancy increased nearly five-fold over the past decade. Despite this, obstetric providers are less likely to treat pregnant women with medication for OUD than non-obstetric providers (75% vs 91%). A major reason is many obstetricians feel unprepared to prescribe medication for opioid use disorder (MOUD). Education and support may increase prescribing and overall comfort in delivering care for pregnant women with OUD, but optimal models of education and support are yet to be determined. METHODS AND ANALYSIS: We describe the rationale and conduct of a matched-pair cluster randomized clinical trial to compare the effectiveness of two models of support for reproductive health clinicians to provide care for pregnant and postpartum women with OUD. The primary outcomes of this trial are patient treatment engagement and retention in OUD treatment. This study compares two support models: 1) a collaborative care approach, based upon the Massachusetts Office-Based-Opioid Treatment Model, that provides practice-level training and support to providers and patients through the use of care managers, versus 2) a telesupport approach based on the Project Extension for Community Healthcare Outcomes, a remote education model that provides mentorship, guided practice, and participation in a learning community, via video conferencing. DISCUSSION: This clustered randomized clinical trial aims to test the effectiveness of two approaches to support practitioners who care for pregnant women with an OUD. The results of this trial will help determine the best model to improve the capacity of obstetrical providers to deliver treatment for OUD in prenatal clinics. TRIAL REGISTRATION: Clinicaltrials.gov trial registration number: NCT0424039.

OBJECTIVE: The time during which adolescents and young adults (AYAs) living with Type 1 Diabetes (T1D) transition from pediatric to adult care is associated with blood sugar levels outside of target ranges, care gaps, and an increased risk of acute diabetes complications. The aim of this study was to understand (1) the perspectives of AYAs and providers about the strengths, challenges, and opportunities of transition care and (2) the role of digital technologies in supporting the transition to adult care. Research Design and Methods. We conducted a qualitative descriptive study that involved 43 semistructured interviews in French or English with AYA living with T1D (aged 16-25; n = 22) and pediatric or adult diabetes health care providers (HCPs) (n = 21). RESULTS: We identified three themes. First, transition care is not standardized and varies widely, and there is a lack of awareness of transition guidelines. Second, virtual care can simultaneously hinder and help relationship-building between providers and AYA. Third, AYAs value a holistic approach to care; both HCPs and AYA highlighted the opportunity to better support overall mental wellbeing. CONCLUSIONS: The design of digital technologies to support T1D transition care should consider methods for standardizing holistic care delivery and integrating hybrid diabetes care visits to support access to transition care. These findings can inform future transition intervention development that leverages existing transition guidelines, targets holistic care model integration, and considers quantitative diabetes metrics in conjunction with broader life experiences of AYA when providing transition care.

Public health emergencies, including climate-related and manmade disasters such as active shooter incidents, occur regularly in the United States. A comprehensive approach is needed to ensure that children's mental health needs are adequately addressed following disasters. This article summarizes the latest evidence on how health systems can effectively address children's unique developmental, social, emotional, and behavioral needs in the context of disasters. To do so requires the integration of mental health considerations throughout all disaster phases, including preparedness, response, and recovery. We discuss the role of traditional emergency response systems and emerging models for responding to mental health crises. These include the national children's disaster mental health concept of operations and specific resources such as crisis lines, mobile crisis units, and telemental health. To achieve a broader reach in addressing children's mental health needs during disasters, health systems can foster a "pediatric disaster system of care" by partnering with community touch points such as schools, faith-based organizations, public health, and law enforcement. Unique considerations during disasters are required to maintain access to care for children with preexisting behavioral health conditions. During disasters, attention is needed to promote equitable identification of mental health needs and linkage to services, particularly for minoritized groups and children living in rural, frontier, and high-poverty areas. Strategies to address children's mental health needs during disasters include the provision of psychological first aid, screening for and triaging mental health needs, and stepped care approaches that progressively allocate higher-intensity evidence-based treatments to children with greater and enduring needs.

While the future of college student mental health is leaning towards systemic-level integrated behavioral health care models, existing mental health support for science, technology, engineering, and mathematics (STEM) students remains highly individual. The REDFLAGS Model is a mental health resource comprising an acronym of warning signs that suggest a college student might be struggling with mental distress. The aim of this study was to test the utility of The REDFLAGS Model, with a large sample of STEM students (N = 358). Results revealed support for the latent dimensionality of The REDFLAGS Model among a large sample of STEM students. Results also demonstrated that higher recognition of the items on The REDFLAGS Model as warning signs for mental distress was a significant predictor of peer-to-peer referrals to counseling among STEM students. Additionally, STEM students with help-seeking histories and those who identified as female were more likely to recognize the items on The REDFLAGS Model as warning signs of mental distress than those without help-seeking histories and men, respectively. Collectively, results indicated that The REDFLAGS Model has potential to provide college counselors with an empirically supported framework for supporting STEM student mental health. It is available at no cost and can be shared in print or digital formats.