Protection motivation theory and cigarette smoking among vocational high school students in China:a cusp catastrophe modeling analysis

Background: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.Methods: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.Results: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.Conclusion: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.

Federated learning-outcome prediction with multi-layer privacy protection

Learning-outcome prediction(LOP)is a long-standing and critical problem in educational routes.Many studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection issue.To this end,this study proposes a distributed grade prediction model,dubbed FecMap,by exploiting the federated learning(FL)framework that preserves the private data of local clients and communicates with others through a global generalized model.FecMap considers local subspace learning(LSL),which explicitly learns the local features against the global features,and multi-layer privacy protection(MPP),which hierarchically protects the private features,including model-shareable features and not-allowably shared features,to achieve client-specific classifiers of high performance on LOP per institution.FecMap is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part,a local part,and a classification head in clients and averaging the global parts from clients on the server.To evaluate the FecMap model,we collected three higher-educational datasets of student academic records from engineering majors.Experiment results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP,compared with the state-of-the-art models.This study makes a fresh attempt at the use of federated learning in the learning-analytical task,potentially paving the way to facilitating personalized education with privacy protection.