你想促进回忆,感知或行为吗? 最好的数据可视化取决于沟通目标
Jessica S Ancker1, Natalie C Benda2, Brian J Zikmund-Fisher3,4,5
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
概括
制定明确的结果措施和数据可视化任务至关重要. 这种方法有助于创建有效的健康沟通工具,通过专注于所需的受众认知,感受和行为.
科学领域:
- 卫生沟通健康沟通
- 信息可视化 信息可视化
- 人与计算机的交互
背景情况:
- 数据可视化对于理解健康数据至关重要,但缺乏实际设计指导.
- 现有的评估方法往往过于简化了"理解"或"解释"等结果.
- 需要更细致的方法来评估可视化有效性.
研究的目的:
- 为评估健康数据可视化的结果措施和任务提出新的分类法.
- 为了指导对健康数字通信文献的系统审查.
- 让设计师能够评估可视化功能如何影响观众的认知,感受和行为.
主要方法:
- 开发新的分类系统,用于结果测量和任务.
- 将这些分类学应用于对健康传播可视化研究的系统审查.
- 专注于特定的受众认知,感受和行为作为评估标准.
主要成果:
- 这些分类学为详细分析可视化效率提供了一个框架.
- 这种细分的方法对于从研究中获得可操作的见解至关重要.
- 确定了特定的可视化特征,支持或抑制所需的受众结果.
结论:
- 为了有效的健康数据可视化设计和评估,需要基于结果的细粒度方法.
- 开发的分类学有助于创建更具包容性和影响力的健康传播.
- 这项研究弥合了可视化研究和实际设计应用之间的差距.
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