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- W2423805647 abstract "Tobacco use is one of the greatest public health problems worldwide and the hazards of cigarette smoking to public health call for better recognition of cigarette smoking behaviors to guide evidence-based policy. Protection motivation theory (PMT) provides a conceptual framework to investigate tobacco use. Evidence from diverse sources implies that the dynamics of smoking behavior may be quantum in nature, consisting of an intuition and an analytical process, challenging the traditional linear continuous analytical approach. In this study, we used cusp catastrophe, a nonlinear analytical approach to test the dual-process hypothesis of cigarette smoking.Data were collected from a random sample of vocational high school students in China (n = 528). The multivariate stochastic cusp modeling was used and executed with the Cusp Package in R. The PMT-based Threat Appraisal and Coping Appraisal were used as the two control variables and the frequency of cigarette smoking (daily, weekly, occasional, and never) in the past month was used as the outcome variable.Consistent with PMT, the Threat Appraisal (asymmetry, α1 = 0.1987, p < 0.001) and Coping Appraisal (bifurcation, β2 = 0.1760, p < 0.05) significantly predicted the smoking behavior after controlling for covariates. Furthermore, the cusp model performed better than the alternative linear and logistic regression models with regard to higher R2 (0.82 for cusp, but 0.21 for linear and 0.25 for logistic) and smaller AIC and BIC.Study findings support the conclusion that cigarette smoking in adolescents is a quantum process and PMT is relevant to guide studies to understand smoking behavior for smoking prevention and cessation." @default.
- W2423805647 created "2016-06-24" @default.
- W2423805647 creator A5031099164 @default.
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- W2423805647 date "2016-06-15" @default.
- W2423805647 modified "2023-10-11" @default.
- W2423805647 title "Protection motivation theory and cigarette smoking among vocational high school students in China: a cusp catastrophe modeling analysis" @default.
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- W2423805647 doi "https://doi.org/10.1186/s41256-016-0004-9" @default.
- W2423805647 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5675066" @default.
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- W2423805647 hasPublicationYear "2016" @default.
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