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      Breadth of Media Scanning Leads to Vaping among Youth and Young Adults: Evidence of Direct and Indirect Pathways from a National Longitudinal Survey

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          Abstract

          Electronic cigarette use among youth and young adults has reached an epidemic proportion of growth. This study examined the direct and indirect effects of breadth of media scanning about e-cigarette use on subsequent vaping behavior through interpersonal communication and changes in descriptive norm perceptions. We conducted a nationally representative longitudinal phone survey of 13–25 year olds from June 2014 to March 2017, with 11,013 respondents who completed a baseline survey, among which 3,212 completed a follow-up six months later. The results from both cross-sectional and lagged analyses provided robust evidence to suggest that passive routine exposure to e-cigarette use content from more media outlets predicted increased likelihood of vaping among youth and young adults. High scanners were about twice as likely to vape as non-scanners (17% versus 9%). Mediation models using bootstrapping procedures found that breadth of scanning predicted higher descriptive norm perceptions which were associated with subsequent vaping; in addition, interpersonal communication mediated the relationship between breadth of scanning and changes in descriptive norm perceptions. These findings highlight the important roles of scanning, norm perceptions and interpersonal discussions in shaping cognition and behavior changes. The results also suggest an overall pro-e-cigarette public communication environment, which warrants further examination.

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          An Introduction to the Bootstrap

          Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
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            Living With Television: The Violence Profile

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              How many bootstrap replicates are necessary?

              Phylogenetic bootstrapping (BS) is a standard technique for inferring confidence values on phylogenetic trees that is based on reconstructing many trees from minor variations of the input data, trees called replicates. BS is used with all phylogenetic reconstruction approaches, but we focus here on one of the most popular, maximum likelihood (ML). Because ML inference is so computationally demanding, it has proved too expensive to date to assess the impact of the number of replicates used in BS on the relative accuracy of the support values. For the same reason, a rather small number (typically 100) of BS replicates are computed in real-world studies. Stamatakis et al. recently introduced a BS algorithm that is 1 to 2 orders of magnitude faster than previous techniques, while yielding qualitatively comparable support values, making an experimental study possible. In this article, we propose stopping criteria--that is, thresholds computed at runtime to determine when enough replicates have been generated--and we report on the first large-scale experimental study to assess the effect of the number of replicates on the quality of support values, including the performance of our proposed criteria. We run our tests on 17 diverse real-world DNA--single-gene as well as multi-gene--datasets, which include 125-2,554 taxa. We find that our stopping criteria typically stop computations after 100-500 replicates (although the most conservative criterion may continue for several thousand replicates) while producing support values that correlate at better than 99.5% with the reference values on the best ML trees. Significantly, we also find that the stopping criteria can recommend very different numbers of replicates for different datasets of comparable sizes. Our results are thus twofold: (i) they give the first experimental assessment of the effect of the number of BS replicates on the quality of support values returned through BS, and (ii) they validate our proposals for stopping criteria. Practitioners will no longer have to enter a guess nor worry about the quality of support values; moreover, with most counts of replicates in the 100-500 range, robust BS under ML inference becomes computationally practical for most datasets. The complete test suite is available at http://lcbb.epfl.ch/BS.tar.bz2, and BS with our stopping criteria is included in the latest release of RAxML v7.2.5, available at http://wwwkramer.in.tum.de/exelixis/software.html.
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                Author and article information

                Journal
                Journal of Health Communication
                Journal of Health Communication
                Informa UK Limited
                1081-0730
                1087-0415
                January 03 2020
                : 1-14
                Affiliations
                [1 ] Department of Communication Studies, University of Georgia, Athens, Georgia, USA
                [2 ] Department of Prevention Research, IFT Institut für Therapieforschung, Munich, Germany
                [3 ] Department of Communication Studies, Texas Christian University, Fort Worth, Texas, USA
                [4 ] Annenberg School for Communication, University of Pennsylvania, Philadelphia, Pennsylvania, USA
                [5 ] Department of Medical Ethics & Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
                Article
                10.1080/10810730.2019.1709925
                7138723
                31900063
                cb90ce9f-76c9-4377-ac7f-2ad299317935
                © 2020
                History

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