完善研究问题定义的既定实践,以培养数字时代的跨学科研究技能:使用名义小组技术的共识研究
Jana Sedlakova1,2, Mina Stanikić1,2,3, Felix Gille1,2
1Digital Society Initiative, University of Zurich, Zurich, Switzerland.
JMIR medical education
|January 23, 2025
概括
这项研究提供了工具和建议,以弥合健康研究和数据科学,帮助跨学科教育和数字健康项目的合作. 它强调了共享的术语和可适应的工作流程,以制定有效的研究问题.
科学领域:
- 数字健康研究 数字健康研究
- 数据科学数据科学数据科学
- 跨学科合作 跨学科合作
背景情况:
- 医疗研究中数字数据的使用越来越多,需要将演性健康研究与代性数据科学方法相结合.
- 缺乏结构化的指导阻碍了有效的跨学科合作和数字健康教育.
- 方法的复杂性需要创新的解决方案,以合并不同的研究范式.
研究的目的:
- 为数字健康领域的跨学科教育和合作提供工具和建议.
- 在设计和指导跨学科项目时支持教育工作者,主管和主要研究人员.
- 促进数据科学方法在健康研究中的整合.
主要方法:
- 通过一个词汇表来开发一种共同的术语,以便共同理解.
- 确定了研究问题制定的既定工作流程.
- 检查了结合健康研究和数据科学方法的研究工作流程的调整.
- 通过使用共识方法进行文献搜索和跨学科专家研讨会.
主要成果:
- 创建了专注于跨学科环境的内容,课程,方法和教学风格的工具.
- 建立了一个共享的词汇表来定义共同的术语和概念.
- 拟议的工作流适应整合数据科学,包括承认约束,代方法和FAIR数据原则.
- 通过可重复性和可查找,可访问,可互操作和可重复使用 (FAIR) 数据原则提高研究质量.
结论:
- 研究问题制定对于数字数据项目至关重要.
- 建立共享的术语和使用研究任务可以促进跨学科的理解.
- 工具和建议支持跨学科数字健康项目的培训.
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