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Impact of mealtime social experiences on student consumption of meals at school: a qualitative analysis of caregiver perspectives
No full textFactors and outcomes associated with using scratch‐cooked, organic, and locally grown foods in school meals in California
Get PDFEvaluation of caffeinated beverages sold in a national sample of U.S. middle and high schools
No full textPosition Statement: The Society of Behavioral Medicine Supports Efforts to Decrease Health Disparities Experienced by Persons with Intellectual and Developmental Disabilities
No full textThe Society of Behavioral Medicine (SBM) supports policies that protect persons with intellectual and developmental disabilities from discrimination that impacts their access to equitable healthcare
The implications of the Make Our Children Healthy Again report for child health and obesity in the US
No full textOn 9 September 2025, the Make America Healthy Again (MAHA) Commission released a strategy report on child health, Make Our Children Healthy Again. The commission organised its recommendations around four root causes of poor health among children in the US: poor diet, exposure to environmental chemicals, lack of physical activity and chronic stress, and overmedicalisation. Although reactions among US public health and medical leaders have been mixed—with particular concern about the government’s approach to vaccines—there is potential to find common ground and bipartisan support for many of the commission’s recommendations on nutrition. To improve children’s diets and reduce their risk of obesity, federal funding decisions must also be coordinated with an evidence based approach to developing policies and supporting programmes
A Geospatial Model for Site Response Complexity
No full textOne-dimensional (1D) site response models assume vertically incident SH waves propagating through laterally uniform soil layers. These assumptions, collectively referred to as the SH1D model, are widely used in site-specific ground motion predictions. However, many studies have demonstrated the limitations of 1D site-response analyses. The term “site response complexity” (SRC) refers to the degree of discrepancy between the observed empirical transfer function (ETF) and the theoretical transfer function (TTF) computed with SH1D modeling. We present a geospatial approach to estimate site response complexity using statistical and machine learning methods with globally or regionally available geospatial proxies. Our site response data are from 114 vertical seismometer arrays in Japan’s Kiban-Kyoshin network (KiK-net) used in Kaklamanos and Bradley (2018). The SRC data are calibrated according to the Thompson et al. (2012) taxonomy that relies on two parameters, r (Pearson’s correlation coefficient between the ETF and TTF) and σi (inter-event variability of the ETF). We examine 18 geospatial proxies associated with site stiffness, topography, basin, and saturation conditions. Using the geospatial proxies as explanatory variables, two sets of predictive models are developed: (a) linear regression models for predicting r and σi, separately, and (b) multiclass classification models for site response complexity. The regression results suggest that predicting σi has greater accuracy than predicting r. Our optimal SRC classification model uses the slope-based VS30 (average shear-wave velocity in the upper 30 m), global sedimentary deposit thickness, and global water table depth as explanatory variables, and has classification accuracies of 0.66 and 0.65 against the training and testing datasets, respectively. We generate maps across Japan for r, σi, and SRC class, separately, which can provide first-order approximations of site response complexity, and exhibit clear patterns between SRC class and topography. We conclude that the geospatial modeling approach is promising for evaluating complexity in site response across broad regions
Classifying hotspots mutations for biosimulation with quantum neural networks and variational quantum eigensolver
No full textThe Role of School Uniform Policies A and B: A Comparative Analysis
Get PDFThis qualitative policy analysis capstone reviewed how two school uniform policies across two counties in the Mid-Atlantic region of the United States impact student learning outcomes. The capstone explored the differences in implementing School Uniform Policies A and B across eight Title I public middle schools in Counties A and B. This author reviewed available scholarly articles to examine the features and limitations of school uniform policies and their impact on student learning outcomes. A comparative analysis methodology was used to investigate School Uniform Policies A and B. Secondary data examining School Uniform Policies A and B, demographic information of Counties A and B, and academic statistics of eight sites and how these policies impacted student learning outcomes were analyzed. The analysis was approached using themes of uniform policy implementation and enforcement, financial accessibility, socioeconomic status, cultural inclusivity, and academic performance and attendance. This author considers uniform policies important due to their effect on students’ school experiences and academic success. Understanding the role that school uniform policies play in educational learning outcomes is a primary reason to pursue how they impact learning environments. This research aims to evaluate policies and their impact on student outcomes, teachers, and other stakeholders’ evidence and insight into future research on the impact school uniform policies have through the lens of enforcement