Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions.

Background: Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality. Methods: We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results: We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes. Discussion: Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Identifying important predictors of adolescent suicide ideation, planning, and attempt in low- and middle-income countries.

INTRODUCTION: Over 90% of all adolescent suicides occur in low- and middle-income countries (LMIC), yet the majority of suicide research has focused on primarily high-income countries (HIC). METHOD: Using nationally representative data on 82,494 adolescents from thirty-four LMIC, this research employed machine learning to compare the predictive effects of multiple determinants of suicidal behaviors previously identified in the literature. RESULTS: Results indicate that distinct predictors are present for suicidal ideation, suicidal planning, and suicide attempts in youth living in LMIC as well as shared predictors common to all three behaviors. CONCLUSION: These findings provide insights into the unique needs in global mental health policy and efforts within and across adolescents in LMIC.

Developing STEM Career Identities among Latinx Youths: Collaborative Design, Evaluations, and Adaptations during COVID-19.

In response to the low representation of Latinx adults in STEM occupations, this community-based participatory action research study aims to increase the number of middle school youths developing STEM career identities and entering high school with the intention to pursue STEM careers. The students were provided with summer and after-school activities focusing on network science and career development curricula. Using a quasi-experimental pretest-posttest design and career narratives, this study examined the changes in STEM and career self-efficacy, as well as career identity. The results show improvements in self-efficacy, an increased number of youths with intentions of pursuing future STEM career opportunities, and deeper reflections on their talents and skills after program participation. This paper also describes the program development and implementation in detail, as well as the adaptations that resulted from COVID-19, for scholars and educators designing similar programs. This study provides promising evidence for the quality of STEM and career development lessons in supporting the emergence of a STEM career identity and self-efficacy.

Predictors of suicide ideation among South Korean adolescents: A machine learning approach.

BACKGROUND: The current study developed a predictive model for suicide ideation among South Korean (Korean) adolescents using a comprehensive set of factors across demographic, physical and mental health, academic, social, and behavioral domains. The aim of this study was to address the pressing public health concerns of adolescent suicide in Korea and the methodological limitations of suicidal research. METHODS: This study used machine learning methods (decision tree, logistic regression, naive Bayes classifier) to improve the accuracy of predicting suicidal ideation and related factors among a nationally representative sample of Korean middle school students (N = 6666). RESULTS: Factors within all domains, including demographic characteristics, physical and mental health, and academic, social, and behavioral, were important in predicting suicidal thoughts among Korean adolescents, with mental health being the most important factor. LIMITATIONS: The predictive model of the current research does not infer causality, and there may have been some loss of information due to measurement issues. CONCLUSIONS: Study results provide insights for taking a multidimensional approach when identifying adolescents at risk of suicide, which may be used to further address their needs through intervention programs within the school setting. Considering the cultural stigma attached to disclosing suicidal ideation and behavior, the current study proposes the need for a preventive screening process based on the observation and assessment of adolescents' general characteristics and experiences in everyday life.

Impact of community well-being on individual well-being: A longitudinal multinational study with 155 countries.

The purpose of the current study is to evaluate the longitudinal relationship between subjective community well-being (CWB) (perception of freedom of choice and social support) and subjective individual well-being (IWB) (positive and negative affect). Using the World Happiness Report (2019), this study examined the subjective IWB and subjective CWB among 155 countries across an 8-year period. Using Latent Class Growth Analysis the results indicated that the 155 countries could be classified into three groups-countries reporting high freedom of choice and high social support, low freedom of choice and low social support, and low freedom of choice and high social support. From the results of both a multigroup Growth Mixed Model and a Growth Curve Model, the three groups were found to vary with respect to positive and negative affect with higher positive affect and lower negative affect reported in countries classified as high freedom of choice and high social support, lower positive affect and higher negative affect reported in countries classified as low freedom of choice and low social support, and lower positive and lower negative affect reported in countries classified as low freedom of choice and high social support. These results indicate that country's rated as allowing higher freedom of choice was associated with higher reported positive affect, and country's rated as having stronger social support systems was associated with lower ratings for negative affect.