Zhai Xing, Wang Qinxiang, Nie Yaqing, Han Aiqing, Li Ruifeng
School of Management, Beijing University of Chinese Medicine, Beijing, China.
Xi'an Children's Hospital, Xi'an, China.
Digit Health. 2025 Jul 28;11:20552076251363463. doi: 10.1177/20552076251363463. eCollection 2025 Jan-Dec.
To systematically investigate the key determinants influencing health information quality in social media environments and elucidate their hierarchical relationships, thereby providing evidence-based guidance for quality improvement.
This study employed an innovative integration of meta-ethnography and Decision-Making Trial and Evaluation Laboratory-Interpretive Structural Modeling (DEMATEL-ISM) methodologies. Through systematic extraction and multi-dimensional analysis of influencing factors-including centrality metrics, causal relationships, and hierarchical structures-we developed a comprehensive mechanism model clarifying factor interactions and their cumulative impacts on health information quality enhancement.
Our analysis identified 18 critical factors affecting health information quality, which were categorized into six distinct hierarchical levels through rigorous computational modeling. The results revealed complex cross-level interactions and mutual influences among these determinants. Nine core factors emerged as pivotal: information accuracy, authority orientation, platform reputation, creator expertise, information utility, health information literacy, content originality, source authority, and health concepts.
The findings establish a hierarchical quality improvement framework, suggesting that targeted interventions focusing on the nine core factors can significantly enhance health information quality in social media ecosystems. This study provides both theoretical foundations and practical insights for multi-stakeholder collaborative governance in digital health communication.
系统研究社交媒体环境中影响健康信息质量的关键决定因素,并阐明它们的层次关系,从而为质量改进提供循证指导。
本研究采用了元民族志与决策试验与评价实验室-解释结构模型(DEMATEL-ISM)方法的创新性整合。通过对影响因素的系统提取和多维度分析,包括中心性指标、因果关系和层次结构,我们构建了一个全面的机制模型,阐明了因素之间的相互作用及其对健康信息质量提升的累积影响。
我们的分析确定了18个影响健康信息质量的关键因素,通过严格的计算建模将其分为六个不同的层次级别。结果揭示了这些决定因素之间复杂的跨层次相互作用和相互影响。九个核心因素成为关键因素:信息准确性、权威性导向、平台声誉、创作者专业知识、信息效用、健康信息素养、内容原创性、来源权威性和健康概念。
研究结果建立了一个层次化的质量改进框架,表明针对九个核心因素的有针对性干预可以显著提高社交媒体生态系统中的健康信息质量。本研究为数字健康传播中的多利益相关方协同治理提供了理论基础和实践见解。