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Introduction  Social-emotional development refers to the development of one's abilities to understand, regulate, and express emotions and to establish and maintain successful relationships with peers and adults. Education in the arts has been shown to promote learning these skills, but the relationship between social-emotional development and summer art camp has not been explored. Methods The objective of this study is to determine the potential for social-emotional development in a community-based art day camp. A qualitative thematic analysis of the art camp's curriculum was conducted and compared with current literature regarding opportunities for social-emotional development in arts education and summer camp settings. Results The summer art camp curriculum included practices known to facilitate social-emotional learning in school-aged children. The curriculum data themes identified were performance, art projects, and outdoor activities. All of these themes have been shown to facilitate social-emotional skill building and can be connected to the components of the Collaborative for Academic, Social, and Emotional Learning (CASEL) framework. Conclusions Through the shown benefits of summer camp in combination with the benefits of in-school arts education, art camp provides the unique opportunity to practice self-expression, friend-making, and self-esteem building, all of which can contribute to mental well-being and academic success long-term.

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BACKGROUND: Hospitalization of patients with DKA creates a significant burden on the US healthcare system. While previous studies have identified multiple potential contributors, a comprehensive review of the factors leading to DKA readmissions within the US healthcare system has not been done. This scoping review aims to identify how access to care, treatment adherence, socioeconomic status, race, and ethnicity impact DKA readmission-related patient morbidity and mortality and contribute to the socioeconomic burden on the US healthcare system. Additionally, this study aims to integrate current recommendations to address this multifactorial issue, ultimately reducing the burden at both individual and organizational levels. METHODS: The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O'Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results. RESULTS: A total of 15 articles were retained for analysis. Among the various social factors identified, those related to sex/gender (n = 9) and age (n = 9) exhibited the highest frequency. Moreover, race and ethnicity (n = 8) was another recurrent factor that appeared in half of the studies. Economic factors were also identified in this study, with patient insurance type having the highest frequency (n = 11). Patient income had the second highest frequency (n = 6). Multiple studies identified a link between patients of a specific race/ethnicity and decreased access to treatment. Insufficient patient education around DKA treatment was noted to impact treatment accessibility. Certain recommendations for future directions were highlighted as recurrent themes across included studies and encompassed patient education, early identification of DKA risk factors, and the need for a multidisciplinary approach using community partners such as social workers and dieticians to decrease DKA readmission rates in diabetic patients. CONCLUSION: This study can inform future policy decisions to improve the accessibility, affordability, and quality of healthcare through evidence-based interventions for patients with DM following an episode of DKA.

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Cetoacidosis Diabética , Readmisión del Paciente , Humanos , Readmisión del Paciente/estadística & datos numéricos , Estados Unidos/epidemiología , Cetoacidosis Diabética/terapia , Cetoacidosis Diabética/epidemiología , Factores de Riesgo , Factores Socioeconómicos , Accesibilidad a los Servicios de Salud/estadística & datos numéricos

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Background: Artificial intelligence (AI) algorithms can be applied in breast cancer risk prediction and prevention by using patient history, scans, imaging information, and analysis of specific genes for cancer classification to reduce overdiagnosis and overtreatment. This scoping review aimed to identify the barriers encountered in applying innovative AI techniques and models in developing breast cancer risk prediction scores and promoting screening behaviors among adult females. Findings may inform and guide future global recommendations for AI application in breast cancer prevention and care for female populations. Methods: The PRISMA-SCR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) was used as a reference checklist throughout this study. The Arksey and O'Malley methodology was used as a framework to guide this review. The framework methodology consisted of five steps: (1) Identify research questions; (2) Search for relevant studies; (3) Selection of studies relevant to the research questions; (4) Chart the data; (5) Collate, summarize, and report the results. Results: In the field of breast cancer risk detection and prevention, the following AI techniques and models have been applied: Machine and Deep Learning Model (ML-DL model) (n = 1), Academic Algorithms (n = 2), Breast Cancer Surveillance Consortium (BCSC), Clinical 5-Year Risk Prediction Model (n = 2), deep-learning computer vision AI algorithms (n = 2), AI-based thermal imaging solution (Thermalytix) (n = 1), RealRisks (n = 2), Breast Cancer Risk NAVIgation (n = 1), MammoRisk (ML-Based Tool) (n = 1), Various MLModels (n = 1), and various machine/deep learning, decision aids, and commercial algorithms (n = 7). In the 11 included studies, a total of 39 barriers to AI applications in breast cancer risk prediction and screening efforts were identified. The most common barriers in the application of innovative AI tools for breast cancer prediction and improved screening rates included lack of external validity and limited generalizability (n = 6), as AI was used in studies with either a small sample size or datasets with missing data. Many studies (n = 5) also encountered selection bias due to exclusion of certain populations based on characteristics such as race/ethnicity, family history, or past medical history. Several recommendations for future research should be considered. AI models need to include a broader spectrum and more complete predictive variables for risk assessment. Investigating long-term outcomes with improved follow-up periods is critical to assess the impacts of AI on clinical decisions beyond just the immediate outcomes. Utilizing AI to improve communication strategies at both a local and organizational level can assist in informed decision-making and compliance, especially in populations with limited literacy levels. Conclusions: The use of AI in patient education and as an adjunctive tool for providers is still early in its incorporation, and future research should explore the implementation of AI-driven resources to enhance understanding and decision-making regarding breast cancer screening, especially in vulnerable populations with limited literacy.