前沿与热点

用户网络健康信息搜索对健康风险认知偏差的影响*——基于CGSS数据的实证研究

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  • (1.南京农业大学信息管理学院    江苏南京   210095)
    (2.四川大学公共管理学院   四川成都   610065)
韩正彪,男,南京农业大学信息管理学院教授;戚景琳,女,南京农业大学信息管理学院博士研究生;王云辉,男,四川大学公共管理学院博士研究生。

收稿日期: 2023-08-04

  网络出版日期: 2023-11-17

基金资助

*本文系南京农业大学中央高校基本科研业务费人文社会科学研究基金项目“在线健康社区用户信息交互机理及效率提升研究”(项目编号:SKYC2023013)研究成果之一。

The Impact of User online Health Information Search on Health Risk Cognitive Bias: An Empirical Study Based on CGSS Data

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Received date: 2023-08-04

  Online published: 2023-11-17

摘要

文章旨在揭示用户网络健康信息搜索与健康风险认知偏差之间的关系,以丰富信息行为领域认知偏差的研究成果,并为全面提升人民的健康认知水平提供建议。基于现有研究基础提出相关假设,利用 CGSS2021 数据,运用OLS模型、工具变量法和调节效应模型检验了用户网络健康信息搜索与健康风险认知偏差之间的关系。结果表明,网络健康信息搜索频繁程度显著负向影响健康风险认知偏差,网络健康信息主题搜索强度和网络健康信息搜索效果的调节作用显著。研究结果可为深入探究网络健康信息搜索中健康风险认知偏差的影响机制提供参考,对于发挥网络健康信息搜索的优势、降低用户健康风险认知偏差具有重要的实践意义。

本文引用格式

韩正彪 戚景琳 王云辉 . 用户网络健康信息搜索对健康风险认知偏差的影响*——基于CGSS数据的实证研究[J]. 图书与情报, 2023 , 43(05) : 62 -71 . DOI: 10.11968/tsyqb.1003-6938.2023068

Abstract

The purpose of this study is to reveal the relationship between users' online health information search and perceived health risk bias, to enrich the research results of cognitive bias in the field of information behavior, and to provide suggestions for comprehensively improving people's health cognition level. Based on the existing research foundation, relevant hypotheses were put forward, and the OLS model, the instrument variable method and the moderating effect model were used to test the relationship between users' online health information search and health risk perception bias using CGSS2021 data.The results show that the frequency of  online health information search has a significant negative impact on health risk cognitive bias, and the moderating effect of online health information topic search intensity and online health information search outcome is significant. The results of this paper can provide a reference for exploring the mechanism of health risk cognitive bias in online health information search, and have important practical significance for giving full play to the advantages of online health information search and reducing users' health risk cognitive bias.
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