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Violence is a major public health concern that particularly impacts Black young adults living in under-resourced, urban communities. There is limited research on promotive and protective factors that mitigate the impact of violence exposure on aggressive behavior. This study aims to address this gap by exploring positive factors across the ecological model in a sample of 141 predominantly Black young adults ages 18 to 22 years living in low-income communities. Regression analyses indicated that generally factors at the individual/peer and family level were more likely to be promotive. Additionally, nine significant interactions found in the moderation analyses highlighted a complex relation between ecological protective factors and aggressive behaviors for these young adults. Implications for future empirical work are discussed.

As Social and Emotional Learning (SEL) expands to focus on adolescent populations, the broadly accepted theoretical framework put forth by the Collaborative for Academic, Social, and Emotional Learning (CASEL) should be empirically tested for measurement utility. Using longitudinal data from the 4-H Study of Positive Youth Development, we first tested and validated the five (self-awareness, self-management, social awareness, relationship skills, responsible decision-making) SEL factor model using confirmatory factor analysis (CFA) in a normative sample of 1,717 U.S. fifth grade youth. The model was then subjected to longitudinal measurement invariance testing using CFA models that included the sixth- and seventh-grade samples to confirm SEL as a robust model across these grades. Validity was further evidenced through relation of the SEL model to important youth outcomes (e.g., academic achievement). Relations were significant and in the expected direction. Implications for application of the model to adolescent development are discussed.

BACKGROUND: Readmission within 30 days of hospital discharge is common and costly among end-stage renal disease (ESRD) patients. Little is known about long-term outcomes after readmission. We estimated the association between hospital admissions and readmissions in the first year of dialysis and outcomes in the second year. METHODS: Data on incident dialysis patients with Medicare coverage were obtained from the United States Renal Data System (USRDS). Readmission patterns were summarized as no admissions in the first year of dialysis (Admit-), at least one admission but no readmissions within 30 days (Admit+/Readmit-), and admissions with at least one readmission within 30 days (Admit+/Readmit+).We used Cox proportional hazards models to estimate the association between readmission pattern and mortality, hospitalization, and kidney transplantation, accounting for demographic and clinical covariates. RESULTS: Among the 128,593 Medicare ESRD patients included in the study, 18.5% were Admit+/Readmit+, 30.5% were Admit+/Readmit-, and 51.0% were Admit-. Readmit+/Admit+ patients had substantially higher long-term risk of mortality (HR = 3.32 (95% CI, 3.21-3.44)), hospitalization (HR = 4.46 (95% CI, 4.36-4.56)), and lower likelihood of kidney transplantation (HR = 0.52 (95% CI, 0.44-0.62)) compared to Admit- patients; these associations were stronger than those among Admit+/Readmit- patients. CONCLUSIONS: Patients with readmissions in the first year of dialysis were at substantially higher risk of poor outcomes than either patients who had no admissions or patients who had hospital admissions but no readmissions. Identifying strategies to both prevent readmission and mitigate risk among patients who had a readmission may improve outcomes among this substantial, high-risk group of ESRD patients.

Adolescent consumers need relationships to develop and thrive, and they establish relationships with people and brands across physical and online environments. While there is limited research in marketing on adolescents' online relationships, research from other disciplines examines these relationships and their complex effects. In this article, we offer a multidisciplinary discussion and practical guidance to facilitate adolescents' developmental relationships online. This guidance will support various stakeholders (including adolescent consumers, content creators, researchers, and policymakers) who are interested in improving the online environment to promote positive adolescent development. [ABSTRACT FROM AUTHOR]

Objectives were (a) to understand a community‐informed narrative, as told by community members (CMs) and community partners (CPs), about the strengths, experiences, and perspectives of public housing communities; and (b) to analyze similarities and differences between CMs' and CPs' experiences and perspectives. Qualitative interviews were conducted with 22 CMs of public housing (ages 26–58, 100% female caregivers, 96% Black, 4% multiethnic) and 43 CPs (ages 28–78, 67.4% female, 81.4% Black and African American). Four themes were derived from the CM and CP interviews: (1) counters to public narratives, (2) disinvestment begets disinvestment, (3) community conditions should be better, and (4) community cohesion and connection. Findings from this study present community‐centered narratives and experiences that were counter to stereotyped public narratives and could influence public perceptions and behavior to inform policy changes related to improving living conditions and supporting CMs in public and low‐income housing communities. [ABSTRACT FROM AUTHOR]

Positive development models shift focus for intervention from avoiding problems, deficits, or psychopathology to promoting skills, assets, and psychological well-being as the critical interests in development and intervention. The field can be characterized as multiple parallel lines of empirical inquiry from four frameworks: Social Competence, Social and Emotional Learning, Positive Youth Development, and Positive Psychology. This review is meant to organize understanding of implications for intervention by comparing these four frameworks along several dimensions including (1) key conceptual frameworks and constructs, (2) populations of interest, (3) measurement practices, and (4) intervention design and content implications to point out areas of overlap and distinction. Furthermore, the authors provide suggestions for the advancement beyond isolated scientific inquiry and unclear overlap and distinction, toward a more integrated approach to study and intervention design that promotes positive development.

Background: Use of intensive longitudinal methods (e.g. ecological momentary assessment, passive sensing) and machine learning (ML) models to predict risk for depression and suicide has increased in recent years. However, these studies often vary considerably in length, ML methods used, and sources of data. The present study examined predictive accuracy for depression and suicidal ideation (SI) as a function of time, comparing different combinations of ML methods and data sources. Methods: Participants were 2459 first-year training physicians (55.1% female; 52.5% White) who were provided with Fitbit wearable devices and assessed daily for mood. Linear [elastic net regression (ENR)] and non-linear (random forest) ML algorithms were used to predict depression and SI at the first-quarter follow-up assessment, using two sets of variables (daily mood features only, daily mood features + passive-sensing features). To assess accuracy over time, models were estimated iteratively for each of the first 92 days of internship, using data available up to that point in time. Results: ENRs using only the daily mood features generally had the best accuracy for predicting mental health outcomes, and predictive accuracy within 1 standard error of the full 92 day models was attained by weeks 7–8. Depression at 92 days could be predicted accurately (area under the curve >0.70) after only 14 days of data collection. Conclusions: Simpler ML methods may outperform more complex methods until passive-sensing features become better specified. For intensive longitudinal studies, there may be limited predictive value in collecting data for more than 2 months. [ABSTRACT FROM AUTHOR]

Violence is a major public health concern that particularly impacts Black young adults living in under-resourced, urban communities. There is limited research on promotive and protective factors that mitigate the impact of violence exposure on aggressive behavior. This study aims to address this gap by exploring positive factors across the ecological model in a sample of 141 predominantly Black young adults ages 18 to 22 years living in low-income communities. Regression analyses indicated that generally factors at the individual/peer and family level were more likely to be promotive. Additionally, nine significant interactions found in the moderation analyses highlighted a complex relation between ecological protective factors and aggressive behaviors for these young adults. Implications for future empirical work are discussed. [ABSTRACT FROM AUTHOR]