Despite minimum drinking age laws, underage college students engage in high levels of risky drinking and reach peak lifetime levels of alcohol dependence. A group of presidents of universities and colleges has argued that these laws promote disrespect for laws in general, and do not prevent drinking or related negative consequences. However, no study has investigated the policy-relevant question of whether students who endorse a personal responsibility to obey drinking laws, regardless of their opinions about the laws, are less likely to drink or to experience negative consequences. Therefore, we compared endorsers to non-endorsers, controlling for race, gender, and baseline outcomes, at two universities (Ns = 2007 and 2027). Neither sample yielded a majority (49% and 38% endorsement), but for both universities, all 17 outcome measures were significantly associated with endorsement across all types of analyses. Endorsers were less likely to drink, drank less, engaged in less high-risk behavior (e.g., heavy/binge drinking), and experienced fewer harms (e.g., physical injury), even when controlling for covariates. Racial/ethnic minority groups were more likely to endorse, compared to White students. By isolating a small window of time between high school and college that produces large changes in drinking behavior, and controlling for covariates, we can begin to hone in on factors that might explain relations among laws, risky behaviors, and harms. Internalization of a social norm to adhere to drinking laws could offer benefits to students and society, but subsequent research is needed to pin down causation and causal mechanisms.
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http://dx.doi.org/10.1037/a0032538 | DOI Listing |
JAMA Netw Open
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Department of Public Health and Preventive Medicine, State University New York (SUNY) Upstate Medical University, Syracuse, New York.
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Department of Population Health Sciences, Spencer Fox Eccles School of Medicine at the University of Utah, Salt Lake City, UT, USA.
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Phys Eng Sci Med
January 2025
School of Physics, Mathematics and Computing, The University of Western Australia, Crawley, WA, Australia.
Artificial Intelligence (AI) based auto-segmentation has demonstrated numerous benefits to clinical radiotherapy workflows. However, the rapidly changing regulatory, research, and market environment presents challenges around selecting and evaluating the most suitable solution. To support the clinical adoption of AI auto-segmentation systems, Selection Criteria recommendations were developed to enable a holistic evaluation of vendors, considering not only raw performance but associated risks uniquely related to the clinical deployment of AI.
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View Article and Find Full Text PDFEur Radiol Exp
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Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, GA, USA.
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