一个用于预测影响WeChat健康信息回避因素的模型
Minghong Chen1, Xiumei Huang1, Yinger Wu1
1School of Information Management, Sun Yat-Sen University, Guangzhou, China.
Digital health
|February 17, 2025
概括
在微信上避免个性化健康信息受到个人,信息和社会因素的影响. 了解这些驱动因素是改善健康决策和信息服务的关键.
科学领域:
- 卫生沟通健康沟通
- 信息科学 信息科学 信息科学
- 社会科学 社会科学 社会科学
背景情况:
- 微信是个性化的健康信息的主要来源.
- 避免这些信息会影响健康决策.
- 了解避免因素对于健康素养至关重要.
研究的目的:
- 确定影响个人健康信息避免在微信的因素.
- 制定这些因素的等级框架.
- 分析因素之间的关系和依赖关系.
主要方法:
- 混合方法结合了半结构面试和接地理论.
- 解释性结构建模 (ISM) 用于层次框架的开发.
- 影响矩阵 - 应用到一个分类的交叉乘法 (MICMAC) 用于因子分析.
主要成果:
- 20个预测因素分为个人,信息和社会因素.
- 确定了一个三层框架:根 (健康特征,认知),中间 (决策认知,信息,社会) 和顶部 (负面情绪,情感因素).
- 健康特征和认知表现出强烈的驱动力;负面情绪和情感因素表现出高度依赖.
结论:
- 解决了关于避免个性化健康信息的研究缺口.
- 提供了对影响微信回避行为因素的见解.
- 有助于加强个人健康信息管理和微信的健康信息服务.
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