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Wu, Anise M. S.; Lau, Joseph T. F.; Cheng, Kit-man; Law, Rita W.; Tse, Vincent W. S.; Lau, Mason M. C. – Journal of Early Adolescence, 2018
Internet addiction (IA) is prevalent among adolescents and imposes a serious public health threat. Familial risk and protective factors may co-exist and interact with each other to determine IA. We conducted a cross-sectional survey among 9,618 Secondary 1 to 4 students in Hong Kong, China. About 16% of the surveyed students were classified as…
Descriptors: Risk, Addictive Behavior, Multivariate Analysis, Intervention
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Dobbs, Page Daniel; Hammig, Bart; Henry, Leah Jean – Health Education Journal, 2017
Objective: Between 2013 and 2014, past 30-day use of e-cigarettes increased from 4.5% to 13.4% among US high school students aged 9-19 years. We sought to examine the influence of perceived addiction and harm of e-cigarettes on e-cigarette use among adolescents. Design: Self-reported use and perception of harm of e-cigarettes were assessed using a…
Descriptors: Smoking, Adolescents, Addictive Behavior, Health Behavior
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Cheung, Nicole W. T. – Youth & Society, 2016
Despite recent concerns over youthful problem gambling, few gambling studies have looked into Asian adolescent populations. This study of a stratified, random sample of high school students in Hong Kong is designed to estimate the prevalence of gambling pathology among Chinese adolescents and to examine the relationships between social strain,…
Descriptors: Foreign Countries, Games, Probability, Addictive Behavior
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Goldstein, Abby L.; Faulkner, Breanne; Cunningham, Rebecca M.; Zimmerman, Marc A.; Chermack, Stephen; Walton, Maureen A. – International Journal of Mental Health and Addiction, 2013
The current study examined the application of resilience theory to adolescent gambling using Latent Class Analysis (LCA) to establish subtypes of adolescent gamblers and to explore risk and promotive factors associated with gambling group membership. Participants were a diverse sample of 249 adolescents ages 14 to 18 (30.1 % female, 59.4 % African…
Descriptors: Adolescents, Addictive Behavior, Resilience (Psychology), Theories
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Brunelle, Natacha; Tremblay, Joël; Blanchette-Martin, Nadine; Gendron, Annie; Tessier, Mélanie – Journal of Child & Adolescent Substance Abuse, 2014
A total of 726 youths presenting a severe addiction were evaluated at the time of their request for services in an addiction rehabilitation center. Results show that the more delinquent youths have a more severe profile of substance use, thus confirming the strong association between psychoactive substance (PAS) use severity and delinquency. Girls…
Descriptors: Correlation, Drug Abuse, Delinquency, Adolescents
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Martin, Gina; Macdonald, Scott; Ishiguro, Sonya – International Journal of Mental Health and Addiction, 2013
This study assessed the relative importance of problem gambling, cocaine use and selected psychosocial characteristics in predicting criminal convictions. A self-administered questionnaire was completed by clients when admitted to a treatment program for a primary problem with cocaine (n = 300), gambling (n = 199), or tobacco (n = 249). The…
Descriptors: Addictive Behavior, Smoking, Adolescents, Measures (Individuals)
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Lamb, R. J.; Kirby, Kimberly C.; Morral, Andrew R.; Galbicka, Greg; Iguchi, Martin Y. – Journal of Consulting and Clinical Psychology, 2010
Objective: Contingency management (CM) effectively treats addictions by providing abstinence incentives. However, CM fails for many who do not readily become abstinent and earn incentives. Shaping may improve outcomes in these hard-to-treat (HTT) individuals. Shaping sets intermediate criteria for incentive delivery between the present behavior…
Descriptors: Smoking, Intervention, Contingency Management, Multivariate Analysis
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Ludden, Alison Bryant; Wolfson, Amy R. – Health Education & Behavior, 2010
Little is known about adolescents' caffeine use, yet caffeinated soda, and more recently coffee and energy drinks, are part of youth culture. This study examines adolescents' caffeine use and, using cluster analysis, identifies three groups of caffeine users who differed in their reasons for use, expectancies, and sleep behaviors. In this high…
Descriptors: Sleep, Adolescents, Multivariate Analysis, Stimulants