视觉化YouTube评论员对美国医疗保健系统的概念:基于证据的政策制定语义网络分析方法
Lana V Ivanitskaya1, Elina Erzikova2
1Health Administration, School of Health Sciences, The Herbert H. and Grace A. Dow College of Health Professions, Central Michigan University, Mount Pleasant, MI, United States.
JMIR infodemiology
|February 11, 2025
概括
这项研究使用语义网络方法分析了YouTube对美国医疗保健的评论,揭示了有关政策,获取和质量的复杂讨论. 交互式可视化有助于决策者了解公众对医疗改革的不同观点.
科学领域:
- 社交媒体分析.
- 卫生政策研究 卫生政策研究
- 网络科学 网络科学
背景情况:
- 从社交媒体噪音中提取有意义的见解对决策者来说是一个重大挑战.
- 了解关于医疗保健等复杂主题的公共话语需要先进的分析方法.
研究的目的:
- 评估一种语义网络方法,用于可视化美国医疗保健系统的社交媒体话语.
- 创建对美国医疗保健的公众评论的交互可视化.
主要方法:
- 重新设计的VOSviewer软件分析了53个视频 (2014-2023) 中的179,193条关于美国医疗保健的YouTube评论.
- 采用叠加增强的语义网络分析来映射评论结构和内容.
- 检查的术语共发生,网络集群和自定义信息层 (叠加层).
- 评估网络的稳定性,代表性,相关性,准确性和决策支持实用性.
主要成果:
- 揭示了关于疾病,健康,医疗服务,意识形态,医疗改革,市场监管,保险,劳动力,牙科护理和等待时间的复杂讨论.
- 在YouTube评论条款和引发视频之间展示了主题对齐,讨论随着时间的推移而持续.
- 确定了医疗保健成本和等待时间的国际比较,表明全球讨论.
- 强调护士短缺和倦怠是重大问题,并指出普遍医疗保健和单一支付系统讨论之间的意识形态差异.
结论:
- YouTube用户参与关键的政策问题:健康的社会决定因素,弱势群体,健康公平,种族主义,质量和获取.
- 提出的语义网络方法适用于卫生政策研究,支持基于证据的决策和理解不同观点.
- 交互式可视化有助于发现大规模模式,并指导定性研究资源配置.
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