Substance use is prevalent among adolescents in the U.S., especially males. Understanding the cross-sectional and longitudinal associations between gender norms and substance use is necessary to tailor substance use prevention messages and efforts appropriately. This study investigates the relationship between adherence to gender-typical behavior (AGB) and substance use from adolescence into young adulthood. Participants in the National Longitudinal Study of Adolescent to Adult Health completed self-report measures on the frequency of binge drinking, cigarette smoking and marijuana use as well as various behaviors and emotional states that captured the latent construct of AGB. Sex-stratified logistic regression models revealed cross-sectional and longitudinal relationships between AGB and high frequency substance use. For example, an adolescent male who is more gender-adherent, compared to less adherent males, has 75% higher odds of high frequency binge drinking in adolescence and 22% higher odds of high frequency binge drinking in young adulthood. Sex-stratified multinomial logistic regression models also revealed cross-sectional and longitudinal relationships between AGB and patterns of use. For example, a more gender-adherent adolescent male, compared to one who is less adherent, is 256% more likely to use all three substances in adolescence and 66% more likely to use all three in young adulthood. Cross-sectional and longitudinal results for females indicate greater gender-adherence is associated with lower odds of high frequency substance use. These findings indicate adherence to gender norms may influence substance use behaviors across the developmental trajectory, and inform strategies for prevention efforts.
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http://dx.doi.org/10.1037/men0000088 | DOI Listing |
Arch Sex Behav
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Instituto Nacional de Infectologia Evandro Chagas, Fundação Oswaldo Cruz (INI-Fiocruz), Rio de Janeiro, Brazil.
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Condensed Matter Physics and Materials Science Division, Brookhaven National Laboratory, Upton, NY, USA.
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Department of Trauma Surgery and Orthopedics, Goethe University, University Hospital, Frankfurt, Germany.
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Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Chatbot-based multimodal AI holds promise for collecting medical histories and diagnosing ophthalmic diseases using textual and imaging data. This study developed and evaluated the ChatGPT-powered Intelligent Ophthalmic Multimodal Interactive Diagnostic System (IOMIDS) to enable patient self-diagnosis and self-triage. IOMIDS included a text model and three multimodal models (text + slit-lamp, text + smartphone, text + slit-lamp + smartphone).
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