ArticleObesity science & practice2025
Mixed Methods to Assess Sex Differences in Weight Bias Internalization Among Patients With Obesity.
Article in Obesity science & practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Sex differences in the influence of weight bias internalization on preferences for telehealth utilization among people with obesity.International journal of obesity (2005) · 2026Article
- Mixed Methods to Assess Sex Differences in Weight Bias Internalization Among Patients With Obesity.Obesity science & practice · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Background: Individuals with obesity frequently encounter weight bias, which can contribute to the internalization of negative weight-related attitudes. This study examined sex differences in self-reported weight bias internalization among a racially and ethnically diverse sample of adults with obesity engaged in treatment. Methods: Mixed method approach was used. Weight bias internalization was assessed quantitatively using the 11-item weight bias internalization scale (WBIS) and qualitatively through in-depth interviews. Sex differences were examined with univariate and multivariate linear regressions. Interviews were thematically analyzed to explore sex differences. Results: Quantitative analysis ( Conclusion: Quantitative analyses indicated no statistically significant sex differences in weight bias internalization, and qualitative findings revealed no substantial conceptual differences between male and female participants. Future research should explore socioecological factors such as race/ethnicity, relationship status, and employment to identify populations at greater risk and inform targeted strategies for improving health outcomes.
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Registered trials
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