授权个人采用人工智能寻找健康信息：香港用户的潜在特征分析

IF 4.9 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Social Science & Medicine Pub Date : 2025-04-10 DOI:10.1016/j.socscimed.2025.118059

Jingyuan Shi , Xiaoyu Xia , Huijun Zhuang , Zixi Li , Kun Xu

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引用次数: 0

摘要

利用人工智能寻找健康信息是一种新颖的行为，因此，制定有效的沟通策略以优化人工智能在这一领域的应用提出了挑战。为了奠定基础，需要进行研究，绘制出用户在使用人工智能方面的行为基础，因为了解用户的需求、关注点和观点可以为在这种情况下设计有针对性和有效的沟通策略提供信息。目的在计划风险信息寻求模型和信息寻求综合模型的指导下，研究社会心理因素（即态度、感知描述性和禁令性规范、自我效能感、技术焦虑）和信息载体相关因素（即对人工智能的信任和感知准确性）如何塑造用户的潜在特征。此外，我们探讨了人口统计属性和人类中心主义的个体差异如何预测这些用户档案的成员资格。方法我们对香港1051名有人工智能经验的用户进行定额抽样调查。使用潜在剖面分析来检验用户的剖面模式。采用层次多元逻辑回归来检验个体差异如何预测这些用户档案中的成员资格。结果潜在剖面分析揭示了五种异质剖面，我们将其标记为“谨慎的接近者”、“随意的调查者”、“忧虑的温和派”、“冷漠的旁观者”和“焦虑的探索者”。每个剖面都与人口统计学属性和/或人类中心主义方面的个体差异相关的特定预测因子相关联。结论研究结果促进了对使用人工智能进行卫生信息搜索的理论理解，提供了理论驱动的策略，使用户能够做出明智的决策，并为优化人工智能技术的采用提供了见解。

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Empowering individuals to adopt artificial intelligence for health information seeking: A latent profile analysis among users in Hong Kong

Rationales

Using AI for health information seeking is a novel behavior, and as such, developing effective communication strategies to optimize AI adoption in this area presents challenges. To lay the groundwork, research is needed to map out users' behavioral underpinnings regarding AI use, as understanding users’ needs, concerns and perspectives could inform the design of targeted and effective communication strategies in this context.

Objective

Guided by the planned risk information seeking model and the comprehensive model of information seeking, our study examines how socio-psychological factors (i.e., attitudes, perceived descriptive and injunctive norms, self-efficacy, technological anxiety) and factors related to information carriers (i.e., trust in and perceived accuracy of AI), shape users’ latent profiles. In addition, we explore how individual differences in demographic attributes and anthropocentrism predict membership in these user profiles.

Methods

We conducted a quota-sampled survey with 1051 AI-experienced users in Hong Kong. Latent profile analysis was used to examine users’ profile patterns. The hierarchical multiple logistic regression was employed to examine how individual differences predict membership in these user profiles.

Results

The latent profile analysis revealed five heterogeneous profiles, which we labeled “Discreet Approachers,” “Casual Investigators,” “Apprehensive Moderates,” “Apathetic Bystanders,” and “Anxious Explorers.” Each profile was associated with specific predictors related to individual differences in demographic attributes and/or aspects of anthropocentrism.

Conclusion

The findings advance theoretical understandings of using AI for health information seeking, provide theory-driven strategies to empower users to make well-informed decisions, and offer insights to optimize the adoption of AI technology.

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来源期刊

Social Science & Medicine PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-

CiteScore

9.10

自引率

5.60%

发文量

762

审稿时长

38 days

期刊介绍： Social Science & Medicine provides an international and interdisciplinary forum for the dissemination of social science research on health. We publish original research articles (both empirical and theoretical), reviews, position papers and commentaries on health issues, to inform current research, policy and practice in all areas of common interest to social scientists, health practitioners, and policy makers. The journal publishes material relevant to any aspect of health from a wide range of social science disciplines (anthropology, economics, epidemiology, geography, policy, psychology, and sociology), and material relevant to the social sciences from any of the professions concerned with physical and mental health, health care, clinical practice, and health policy and organization. We encourage material which is of general interest to an international readership.