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Race and Ethnicity as Factors in Healthcare Discrimination in the United States: A Cross-Sectional Study.
J Racial Ethn Health Disparities
January 2025
Medical Group Population Health, University of Utah, Salt Lake City, UT, USA.
Background And Purpose: Discrimination is the unfair or prejudicial treatment of people and groups based on certain characteristics. Discrimination in health care can impede access to quality care for patients and lead to poor health outcomes. This study aimed to investigate factors, including race and ethnicity, associated with discrimination in health care in the United States.
View Article and Find Full Text PDFOver the last decade, especially since the pandemic, more research has been happening online. Conducting research online can create opportunities to include autistic people across the world and make our studies more diverse. However, conducting research online had led to scammers, or people pretending to be autistic, participating in autism research studies.
View Article and Find Full Text PDFThe protective power of dissent? A longitudinal study on cognitive and socio-emotional determinants of COVID-19 vaccine hesitancy among young people in Canada.
Transcult Psychiatry
October 2024
Division of Social and Cultural Psychiatry, McGill University, Montréal, QC, Canada.
COVID-19 has elicited polarized reactions to public health measures, fueling anti-vaccination movements worldwide which indicate that vaccine hesitancy represents a common expression of dissent. We investigate changes in cognitive (i.e.
View Article and Find Full Text PDFWhen Time Is the Enemy: An Initial Test of the Process Model of Patience.
Pers Soc Psychol Bull
October 2024
University of California, Riverside, USA.
The process model of patience attempts to reconcile disparate approaches to understanding patience. This investigation provides an initial test of the tenets of this new theoretical model, which positions impatience as a discrete emotion and patience as a targeted form of emotion regulation. In three studies with diverse samples ( = 1,401; data collected 2022-2023), participants responded to hypothetical scenarios designed to tap into familiar experiences of impatience.
View Article and Find Full Text PDFEnhancing fairness in AI-enabled medical systems with the attribute neutral framework.
Nat Commun
October 2024
The Data Center, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430016, Hubei, China.
Article Synopsis
- Unfairness and inequity in AI usage for healthcare lead to challenges, especially when models perform unequally across different demographic groups due to biases associated with sensitive attributes.
- The Attribute Neutral Framework and its component, the Attribute Neutralizer (AttrNzr), work to remove biased attributes from data to reduce unfairness while improving representation among various subgroups.
- AttrNzr not only enhances fairness in disease diagnosis models (DDMs) while maintaining accuracy but also offers a flexible, model-independent approach that effectively mitigates unfairness during the training phase.
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