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吸烟情境下的信息寻求:综合信息寻求模型(CMIS)的预测因素。

Information seeking in the context of cigarette smoking: predictors from the Comprehensive Model of Information Seeking (CMIS).

机构信息

Valenti School of Communication, University of Houston , Houston, TX, USA.

University of Houston , Houston, TX, USA.

出版信息

Psychol Health Med. 2020 Dec;25(10):1228-1246. doi: 10.1080/13548506.2020.1728348. Epub 2020 Feb 20.

DOI:10.1080/13548506.2020.1728348
PMID:32077752
Abstract

: The CMIS indicates that key variables in actively obtaining information on cigarette smoking are demographics, direct experience, salience, and beliefs, which affects subsequent evaluations and utility of information. : Cross-sectional data were drawn from the HINTS-FDA 2015 national survey in which a stratified random sample of the U.S. postal addresses (N = 3,738) self-administered a mailed paper questionnaire. Path analysis was conducted to test the CMIS. : Age, income, education, sexual orientation, beliefs about behavior change, and salience are significant predictors of perceived utility of information.Direct predictors of information seeking on health effects are comprehension of information (β = .06, 95% CI: .02-.09, p < .05), trust in information sources (β = .23, 95% CI: .18-.276, p < .01), and confidence in obtaining information (β = .10, 95% CI: .047-.160, p < .05). The final model produced fit indices of c = 356.48, df = 24, CFI = .91, RMSEA = .061 (95% CI: .055-.067), R = .098. : The CMIS is a valid theoretical framework in predicting information seeking on cigarette smoking. This study closes a gap in the literature by addressing key factors simultaneously that influence information seeking on health effects and cessation of cigarette smoking.

摘要

:CMIS 表明,主动获取吸烟信息的关键变量包括人口统计学特征、直接经验、显著度和信念,这些因素会影响对信息的后续评估和利用。:横断面数据来自 HINTS-FDA 2015 年全国调查,该调查采用分层随机抽样方法对美国邮政地址(N=3738)进行了邮寄纸质问卷的自我管理。路径分析用于测试 CMIS。:年龄、收入、教育、性取向、对行为改变的信念和显著度是信息感知有用性的重要预测因素。对健康影响信息寻求的直接预测因素是对信息的理解(β=0.06,95%CI:0.02-0.09,p<0.05)、对信息源的信任(β=0.23,95%CI:0.18-0.276,p<0.01)和获取信息的信心(β=0.10,95%CI:0.047-0.160,p<0.05)。最终模型的拟合指数为 c=356.48,df=24,CFI=0.91,RMSEA=0.061(95%CI:0.055-0.067),R=0.098。:CMIS 是预测吸烟信息寻求的有效理论框架。本研究通过同时解决影响健康影响和戒烟信息寻求的关键因素,填补了文献中的空白。

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