Background: Studies showing that adolescents are more likely to smoke if they have friends who smoke typically infer that this is the result of peer influence. However, it may also be due to adolescents choosing friends who have smoking behaviors similar to their own (i.e., selection). One of the most influential studies of influence and selection effects on smoking concluded that these processes contribute about equally to peer group homogeneity in adolescent smoking (Ennett and Bauman, 1994). The goal of this study was to conduct a partial replication of these findings.
Methods: Data are from 1223 participants in the National Longitudinal Study of Adolescent Health. Spectral decomposition techniques identified friendship cliques, which were then used as the unit of analysis to examine influence and selection effects over a one-year period.
Results: Non-smokers were more likely to become smokers if they initially belonged to a smoking (vs. non-smoking) group, and smokers were more likely to become non-smokers if they initially belonged to a non-smoking (vs. smoking) group, indicating an influence effect on both initiation and cessation. Further, group members who changed groups between waves were more likely to select groups with smoking behavior congruent to their own, providing evidence of a selection effect.
Conclusions: While our results generally replicate the group analyses reported by Ennett and Bauman (1994), they suggest that peer influence and selection effects on adolescent smoking may be much weaker than assumed based on this earlier research.
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http://dx.doi.org/10.1016/j.drugalcdep.2009.12.017 | DOI Listing |
Environ Sci Pollut Res Int
January 2025
Department of Geomatics Engineering, Hacettepe University, 06800, Beytepe, Ankara, Türkiye.
This study presents a hybrid methodology for planning green spaces to enhance urban sustainability and livability, evaluating the impacts of climate change on cities. Cities, once accommodating a small population, have become major centers of migration and development since the eighteenth century. Rapid urban growth intensifies infrastructure, environmental, and social challenges.
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January 2025
School of Humanities and Social Sciences, Anhui University of Science and Technology, Huainan, 232001, China.
In this paper, the Hefei metropolitan area is selected as the research object to measure industrial carbon emissions in this area during 2010-2022. The main contribution is to deeply analyze the characteristics of the spatial correlation network of industrial carbon emissions in the Hefei metropolitan area with the modified gravity model and social network analysis(SNA), and to explore the driving factors of its formation with quadratic assignment procedure(QAP). It establishes the foundation for the Hefei metropolitan area to differentiated green city development policies.
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January 2025
Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, Liaoning, China.
The problem of ground-level ozone (O) pollution has become a global environmental challenge with far-reaching impacts on public health and ecosystems. Effective control of ozone pollution still faces complex challenges from factors such as complex precursor interactions, variable meteorological conditions and atmospheric chemical processes. To address this problem, a convolutional neural network (CNN) model combining the improved particle swarm optimization (IPSO) algorithm and SHAP analysis, called SHAP-IPSO-CNN, is developed in this study, aiming to reveal the key factors affecting ground-level ozone pollution and their interaction mechanisms.
View Article and Find Full Text PDFAcad Radiol
January 2025
Medical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 (M.L., M.A., J.K.U., Y.T., C.W., N.P., S.M., D.A.T.). Electronic address:
Rationale And Objectives: Cardiovascular toxicity is a well-known complication of thoracic radiation therapy (RT), leading to increased morbidity and mortality, but existing techniques to predict cardiovascular toxicity have limitations. Predictive biomarkers of cardiovascular toxicity may help to maximize patient outcomes.
Methods: The machine learning optimal biomarker (OBM) method was employed to predict development of cardiotoxicity (based on serial echocardiographic measurements of left ventricular ejection fraction and longitudinal strain) from computed tomography (CT) images in patients with thoracic malignancy undergoing RT.
Oral Oncol
February 2025
Department of Nuclear Medicine, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou 510060, China. Electronic address:
Purpose: To investigate the prognostic value of post-chemoradiotherapy 2-[F]FDG PET/CT in locally advanced nasopharyngeal carcinoma (LANPC) and develop an accurate prognostic model based on the 2-[F]FDG PET/CT results.
Methods: 900 LANPC patients who underwent pretreatment and post-chemoradiotherapy 2-[F]FDG PET/CT from May 2014 to August 2022 were included in the study. We divided the patients into two distinct cohorts for the purpose of our study: a training cohort comprising 506 individuals, included from May 2008 to April 2020, and a validation cohort consisting of 394 individuals, included from May 2020 to August 2022.
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