Weight discrimination is pervasive in American society and impairs quality of life for obese persons. With approximately two-thirds of Americans now overweight or obese, vast numbers of people are vulnerable to weight prejudice and its consequences. Currently, no laws exist to prohibit weight discrimination. This study conducted an online survey with a national sample of 1,001 adults (representing demographics of the United States) to examine public support for six potential legislative measures to prohibit weight discrimination in the United States. Results indicated substantial support (65% of men, 81% of women) for laws to prohibit weight discrimination in the workplace, especially for legal measures that would prohibit employers from refusing to hire, terminate, or deny promotion based on a person's body weight. Laws that proposed extending the same protections to obese persons as people with physical disabilities received the least support, suggesting that Americans may not be in favor of considering obesity as a disability. Findings also highlight specific predictors of support (related to sex, age, education, income, body weight, and political ideology). These findings can be used to inform policy makers in efforts to develop antidiscrimination laws. Such measures will rectify health disparities for overweight Americans and facilitate public health efforts to address obesity.
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http://dx.doi.org/10.1038/oby.2010.126 | DOI Listing |
Sci Rep
January 2025
Stroke Unit, Department of Neurology, Universitat de Lleida, IRBLleida, Hospital Universitari Arnau de Vilanova de Lleida, Avda Rovira Roure, 80, Lleida, 25198, Spain.
Evaluating scales to detect large vessel occlusion (LVO) could aid in considering early referrals to a thrombectomy-capable center in the prehospital stroke code setting. Nevertheless, they entail a significant number of false positives, corresponding to intracranial hemorrhages (ICH). Our study aims to identify easily collectible variables for the development of a scale to differentiate patients with ICH from LVO.
View Article and Find Full Text PDFActa Paediatr
January 2025
INSERM, Clinical Research Department, University Hospital of Nantes, Nantes, France.
Aim: To develop and internally validate a new severity score to more accurately assess the clinical severity forms of acute gastroenteritis (AGE) in children from birth to age 5 years.
Methods: We included children consulting for AGE in the emergency department of the University Hospital of Nantes (March 2017-June 2019). We developed and evaluated a new predictive score (GASTROVIM score) using the classification and regression trees.
Antibodies (Basel)
December 2024
Eli Lilly and Company, Lilly Corporate Center Indianapolis, Indianapolis, IN 46285, USA.
Background: The prediction of human clearance (CL) and subcutaneous (SC) bioavailability is a critical aspect of monoclonal antibody (mAb) selection for clinical development. While monkeys are a well-accepted model for predicting human CL, other preclinical species have been less-thoroughly explored. Unlike CL, predicting the bioavailability of SC administered mAbs in humans remains challenging as contributing factors are not well understood, and preclinical models have not been systematically evaluated.
View Article and Find Full Text PDFFront Psychol
January 2025
Department of Social and Behavioural Medicine, Faculty of Medicine, PJ Safarik University in Kosice, Kosice, Slovakia.
We aimed to assess the role of weight stigma and social support in depression, anxiety, and loneliness controlling for sociodemographic and clinical variables. A total of 189 adults with overweight/obesity were included. Participants were recruited from outpatient clinics by general practitioners which covered all regions of Slovakia.
View Article and Find Full Text PDFFront Neurosci
January 2025
School of Data Science, Lingnan University, Hong Kong SAR, China.
Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.
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