️ 29: Rethinking Agency for Genetic Testing Intention among Latinos — Determining Predictors of Intention for Carrier Screening and Cancer Predisposition Testing

️ Episode 29: Rethinking Agency for Genetic Testing Intention among Latinos — Determining Predictors of Intention for Carrier Screening and Cancer Predisposition Testing In this episode of Base por Base, we explore a pre-proof by Chavez-Yenter and Kaphingst (2025) in Genetics in Medicine that leverages the Integrated Behavioral Model to uncover how attitudes, social norms, and perceived agency drive Latino adults’ intentions to pursue both carrier screening and cancer predisposition testing . Study Highlights:The authors first applied confirmatory factor analysis to refine latent constructs for attitude, norms, and agency, removing indicators with loadings below .500, and then employed structural equation modeling to predict behavioral intention for carrier screening (CS) and cancer predisposition testing (CPT) . Across both outcomes, perceived agency emerged as the strongest positive driver—β = .381 (p < .01) for CS and β = .559 (p < .01) for CPT—while stronger normative beliefs from family and friends boosted CS intention and attitudes showed a modest negative association with CS intention . Sensitivity analyses excluding participants with prior genetic testing upheld these agency and norm effects for carrier screening but attenuated IBM effects for cancer testing . Conclusion:These findings suggest that interventions designed to empower Latino patients—by enhancing their perceived ability to access testing and engaging family and social networks—may be more effective at increasing genetic testing uptake than strategies focused solely on information provision and attitude change . Reference:Chavez-Yenter D, Kaphingst KA. Rethinking Agency for Genetic Testing Intention among Latinos: Determining Predictors of Intention for Carrier Screening and Cancer Predisposition Testing. Genetics in Medicine. 2025. https://doi.org/10.1016/j.gim.2025.101455 License:This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license – https://creativecommons.org/licenses/by/4.0/

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