通过人类在循环中的视频来促进健康素养 YouTube视频的可理解性分类:开发和评估研究研究
Xiao Liu1, Anjana Susarla2, Rema Padman3
1Department of Information Systems, W. P. Carey School of Business, Arizona State University, Tempe, AZ, United States.
Journal of medical Internet research
|April 8, 2025
概括
开发一种自动化的方法来评估YouTube上的健康视频的可理解性,可以显著提高观众参与度. 高度可理解的视频获得更多的浏览量,点赞和评论,帮助专家推优质的患者教育材料.
科学领域:
- 数字健康数字健康
- 卫生沟通健康沟通
- 医疗保健中的人工智能
背景情况:
- 大多数美国成年人使用互联网获取健康信息,但很少有人具备足够的健康素养.
- 在了解在线健康内容方面存在很大的差距,特别是在YouTube等社交媒体平台上.
- 需要自动化方法来为各种人群准备可访问的在线健康信息.
研究的目的:
- 开发一个自动化系统来评估患者教育视频的可理解性,使用患者教育材料评估工具 (PEMAT) 标准.
- 评估视频的可理解性如何影响观众参与度指标.
- 为内容创作者和医疗保健组织提供可操作的见解,以提高对在线健康视频的参与度.
主要方法:
- 采用了结合人类专业知识和机器学习的增强智能方法.
- 患者教育材料评估工具 (PEMAT) 的构造被映射到视频功能和专家注释中.
- 机器学习模型,包括共训练,用于对与糖尿病相关的内容进行视频理解度分类.
主要成果:
- 自动化系统在分类视频可理解性方面取得了高准确性 (F1分数为0.81).
- 高度可理解的视频显示了显著更高的观看次数 (ATE=2.55),喜欢次数 (ATE=2.95) 和评论次数 (ATE=3.10).
- 医疗专家更频繁地推可理解的视频 (72%),而不是根据YouTube算法排名的视频 (40%).
结论:
- 开发了一种可扩展的人类循环算法,以评估YouTube上的健康信息可理解性.
- 该方法有效地将专家知识与算法能力结合起来,以提高内容参与度.
- 该工具帮助医疗专家推教育内容,并指导组织创建更好的患者教育材料.
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