在移动健康的定性分析中使用框架矩阵和主题编码方法:NIRUDAK研究数据
Rochelle K Rosen1, Monique Gainey2, Sabiha Nasrin3
1Center for Behavioral and Preventive Medicine, The Miriam Hospital, Providence, RI, United States.
International journal of qualitative methods
|May 31, 2024
概括
框架矩阵分析 (FMA) 和应用主题分析 (ATA) 为移动健康 (mHealth) 研究提供了明显的优势. FMA有助于减少设计决策的数据,而ATA捕获了细微的用户见解以实现.
科学领域:
- 定性研究方法 定性研究方法
- 医疗信息学 医疗信息学
- 移动健康 (mHealth) 应用程序开发
背景情况:
- 框架矩阵分析 (FMA) 和应用主题分析 (ATA) 是较少引用的定性方法.
- 本研究将FMA和ATA在移动健康 (mHealth) 研究的背景下进行比较.
研究的目的:
- 为了比较FMA和ATA的方法,实用性和结果.
- 为在移动健康研究中有效使用这些定性方法提供建议.
主要方法:
- 通过孟加拉国八个焦点小组讨论,从医生和护士那里收集了定性数据.
- 同样的数据被单独使用FMA和ATA分析.
- 使用FMA分析了关于新型mHealth应用程序用于急性腹管理的关键特征的意见.
- ATA被用来探索对mHealth应用程序的临床实用性和用户态度.
主要成果:
- FMA和ATA对移动健康发展的研究目标和时间表有不同的贡献.
- 当需要特定的编程和设计决策时,FMA对于数据减少是有效的.
- ATA对于理解与应用程序使用,实施,培训和工作流相关的细微问题是有价值的.
结论:
- 无论是FMA还是ATA,都为mHealth设计提供了有价值的,但又不同的见解.
- 建议FMA用于指导特定应用程序设计选择.
- ATA 建议用于告知更广泛的实施策略和用户培训.
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