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  1. Seo DC, Torabi MR, Kim N, Lee CG, Choe S
    Am J Health Behav, 2013 Mar;37(2):199-207.
    PMID: 23026101 DOI: 10.5993/AJHB.37.2.7
    OBJECTIVES: To compare the prevalence and correlates of cigarette smoking among East Asian college students.
    METHODS: Data were collected from college students (N=16,558) in China, Hong Kong, Malaysia, Singapore, South Korea, and Taiwan (response rate: 78%).
    RESULTS: Religion was independently associated with college students' smoking in China (adjusted odds ratio [AOR] = 1.82) and South Korea (AOR = 0.80). Being a heavy drinker and having a higher exposure to secondhand smoke were associated with higher smoking rates (Ps < .001).
    CONCLUSIONS: The East Asian economies show a varied prevalence of college smoking but a similar pattern of relationship with its correlates.
    Study site: 21 institutions in 6 East Asian economies: 3 colleges each from Hong Kong, Malaysia, Singapore, and South Korea; 4 colleges from Taiwan; and 5 colleges from China.
  2. Parkinson CM, Hammond D, Fong GT, Borland R, Omar M, Sirirassamee B, et al.
    Am J Health Behav, 2009 Jul-Aug;33(4):366-75.
    PMID: 19182982
    To characterize smoking beliefs among Thai and Malaysian youth and to examine associations with gender, antismoking media exposure, and smoking status.
  3. Xiong J, Domnic Jacob GA, Xiong JG
    Am J Health Behav, 2023 Feb 28;47(1):165-172.
    PMID: 36945091 DOI: 10.5993/AJHB.47.1.17
    Objectives: In this study, we analyzed negative online public opinion in tertiary hospitals and evaluated corresponding risk by applying the Kaiser Model. Methods: Through data and expert group discussion, combined with the hospital's actual negative online public opinion management, we determined the opinions posing higher risk. The hospital's risk questionnaire for negative opinion was designed based on the Kaiser Model. The whole hospital staff was then trained and investigated. An Excel worksheet was used for statistical analysis and risk calculation.Results: According to the ranking of risk value, the top 5 negative online public opinions were drug supply and demand, in-hospital parking, handling of public health emergencies, the service attitude of hospital guidance staff, and interpretation of medical insurance policies. Conclusion: The hospital needs to revise and improve the emergency response plan for negative online public opinion based on the analysis results and reports of opinion risks. This is helpful for strengthening hospital-level emergency training, improving the hospital's ability to manage negative opinion risks, and promoting the hospital to become passive about negative online public opinion.
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