Objectives: Racial-ethnic differences in physical/mental health are well documented as being associated with disparities; however, emerging conceptual models increasingly suggest that group differences in social functioning and organization contribute to these relationships. There is little work examining whether racial-ethnic groups respond similarly to classic measures of social networks and perceived support and whether there are significant between-groups differences on these measures.
Method: A multisite, cross-sectional study of 2,793 non-Hispanic White (NHW), non-Hispanic Black (NHB), and Hispanic participants was conducted using common measures of social networks and perceived support. A confirmatory factor analytic model was used to test for the invariance of factor covariance and mean structures in a three latent constructs model including social network, social provisions, and interpersonal support. Between-group differences in structural and functional support were assessed.
Results: We established measurement invariance of the latent representations of these measures suggesting that racial-ethnic groups responded comparably. In direct comparisons, Hispanics and NHWs demonstrated similar levels of network structure and support. In contrast, NHWs reported support advantages on a majority of measures compared with NHBs.
Conclusions: Findings support the use of these measures across groups and provide initial support for potential differences in this hypothesized mediator of racial-ethnic health disparities. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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http://dx.doi.org/10.1037/cdp0000283 | DOI Listing |
Heliyon
January 2025
School of Nursing, Research Institute of Nursing Science, Hallym University, Chuncheon, South Korea.
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View Article and Find Full Text PDFPNAS Nexus
January 2025
Max Planck Institute for Intelligent Systems, Tübingen 72076, Germany.
Networked datasets can be enriched by different types of information about individual nodes or edges. However, most existing methods for analyzing such datasets struggle to handle the complexity of heterogeneous data, often requiring substantial model-specific analysis. In this article, we develop a probabilistic generative model to perform inference in multilayer networks with arbitrary types of information.
View Article and Find Full Text PDFJ Hunger Environ Nutr
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Department of Environmental, Occupational and Geospatial Sciences, City University of New York School of Public Health and Health Policy.
The goal of this study is to describe the social networks of Latino immigrants (n=80) in New York City, and how various network features are linked with dietary quality and food insecurity. Participants had higher Healthy Eating Index (HEI) scores if their social networks were more transitive (β = 6.11, <0.
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Department of Orthopaedics and Traumatology, University of Health Sciences, Adana City Training and Research Hospital, Adana, Turkey.
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