Preliminary Conference Agenda

[目的/意义] 本研究通过分析健康焦虑人群的信息认知、信息认知偏差及其影响因素，探索信息认知偏差形成原因与过程，拟发现健康焦虑人群信息认知存在的问题，为网络健康信息管理提供参考。[方法/过程] 采取深度访谈法收集有关健康焦虑人群信息认知的访谈资料，进行扎根理论编码分析，识别健康焦虑人群的信息认知的主要环节、信息认知偏差、影响因素，进行专家咨询与调查问卷分析，对健康焦虑人群的信息认知偏差进行实证检验，分析与解读信息认知偏差、影响因素及它们之间的作用关系，探讨信息认知形成的过程与原因。[结果/结论] 健康焦虑人群信息认知所受的刺激是外部刺激和焦虑敏感性，其信息认知过程包括对信息的感知觉、注意、表征、记忆和推理5个主要环节，在刺激的心理强度、问题解决的思维、后效和知识储备4种主要因素的影响下，形成无法忍受不确定性、灾难性思维、证实性偏差和健康信息注意偏向4种典型的信息认知偏差。

[Purpose/Significance] This article explore the influencing factors of online health information adoption among digital immigrant, in order to improve the current status of health information adoption and utilization, bridge the digital divide, and enhance the digital dividend of health. [Method/Process] This research introduces information ecology theory and social emotional choice theory, using statistical analysis and qualitative comparative analysis to explore the motivations and configuration types of digital immigrant groups adopting online health information.[Result/Discussion] Quantitative analysis found that the emotional valence of information, relationship strength, and future time insight significantly affect the willingness to adopt health information. The moderating effect of future time insight exists, while the impact of technical anxiety is not significant; Qualitative analysis found that there are four types of configurations that generate high willingness to adopt health information, which can be classified into three categories: demand driven, emotional management, and passive acceptance.

摘要:[目的/意义] 从不同信息加工模式下研究在线健康社区用户风险感知行为，发现用户对风险感知的显著影响因素，对进一步帮助我们了解社区用户应对风险的机制，从而采取相应的措施提高用户在线健康社区使用参与率，促进公众健康知识的传播和国内在线健康社区的持续健康发展具有重要意义。[方法/过程] 以双重信息加工为理论基础，构建在不同信息加工模式下信息质量、情感支持、系统质量以及自我效能对风险感知的理论模型，并通过网络平台发放问卷，收集数据。[结果/结论]系统质量和自我效能对启发式和系统式信息加工均有显著的积极影响，而信息质量、情感支持以及用户的启发式的信息加工消极影响了用户信息隐私风险感知。在线健康社区的运营，要重视平台信息质量，增强平台专业化健康信息交流，促进用户的健康信息素养，营造良好的社区交互氛围。

[目的/意义]基于现行医疗数据行业标准中的数据分级结果模糊，且缺乏对分级要素定量测度的现状，通过挖掘医疗隐私文本，从客观认知视角进行医疗数据隐私计量，为验证与改进现行医疗数据分级结果提供参考。[方法/过程]医疗数据行业标准、法律法规、学术论文和泄露案例构成敏感数据识别来源，医疗敏感名词（数据项）、敏感动词和敏感程度词等敏感词汇组成的敏感数据单元构成敏感数据识别模型，敏感词汇的敏感性、语义强度和文本力度等指标构成医疗隐私计量模型。[结果/结论]计量结果表明，医疗应用数据（医疗检验数据、治疗过程数据、医疗记录数据）和健康状况数据（主诉与既往病史、现病史与生活方式、体格检查数据）的隐私性最强、医疗支付数据（医疗费用数据、支付方式数据、医疗保险数据）的隐私性其次、个人属性数据（个人身份数据、人口统计数据、个人联系方式数据）的隐私性最低。

[Purpose/Significance]There is no “common norm or value” of privacy between East and West. Under different cultural perspectives, there are differences in many aspects such as values, communication modes and interaction norms, which can cause cross-cultural differences in privacy. [Method/process]Privacy is placed in a specific realistic situation to explore what people are talking about when they talk about privacy. The content of Weibo and Twitter posts containing “COVID-19” and “privacy” from 2020 to 2022 is captured as a corpus, and the mixed method of structural topic modeling and manual interpretation is adopted for analysis and interpretation. [Result/Conclusion]Privacy is a multi-dimensional concept, which can be interpreted from different dimensions such as individual, technology, law, group, organization and practical practice. Due to the difference of collectivism and individualism between Chinese and Western cultural values, Chinese people are more able to appropriately surrender their personal privacy to public interests when facing the public interest needs of health emergencies. In the English Twitter corpus, personal privacy always occupies a prominent position, and the public pays more attention to privacy, and restricts the government's intervention and management of emergencies through the exercise of negative rights. There are many similarities in people's understanding of privacy in the technical dimension from different cultural perspectives, especially the concern about the risk of privacy disclosure caused by new technologies. The research results can enrich the academic community's understanding of privacy from the macro cultural background and social reality, and also provide a reference for the cross-culture comparative study of privacy.