CUBES: A practical toolkit to measure enablers and barriers to behavior for effective intervention design.
CUBES: A practical toolkit to measure enablers and barriers to behavior for effective intervention design.
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DOI：
10.12688/gatesopenres.12923.2
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发表时间：
2019-01-01
影响因子：
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通讯作者：
Sgaier, Sema K
中科院分区：
文献类型：
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作者：
Engl, Elisabeth;Sgaier, Sema K
全球发展和其他部门的一个紧迫目标往往是了解是什么驱动人们的行为，以及如何影响他们。然而，设计行为改变干预措施往往是一个不系统的过程，由于对情境和感知行为驱动因素的理解不足，以及对有限的研究方法的狭隘关注，这些因素阻碍了行为改变干预措施的设计。我们提出了一个工具包（CUBES）的两个解决方案，以帮助方案达到更有效的干预。首先，我们介绍了一种新的行为框架，这是一个实用的工具，程序结构的潜在驱动程序和匹配相应的干预措施。这一基于证据的框架是通过广泛的跨部门文献研究开发的，并通过在大规模全球发展计划中的应用得到完善。其次，我们提出了一套描述性的，实验性的和模拟的方法，可以增强和扩展在全球发展中常用的方法。由于不是所有的方法都同样适合捕捉不同类型的驱动程序的行为，我们提出了一个决策辅助方法的选择。我们建议现有的常用方法，如观察和调查，使用CUBES作为脚手架，并结合特定类型的驱动程序的验证措施，以全面测试目标行为的所有潜在组成部分。我们还推荐了市场研究、实验心理学和决策科学等领域未充分使用的方法，这些方法可以用于扩展其工具包，并测试关键推动因素和障碍的重要性和影响。CUBES工具包使跨部门的项目能够简化干预措施的概念化、设计和优化过程，并最终改变行为并实现目标结果。
A pressing goal in global development and other sectors is often to understand what drives people's behaviors, and how to influence them. Yet designing behavior change interventions is often an unsystematic process, hobbled by insufficient understanding of contextual and perceptual behavioral drivers and a narrow focus on limited research methods to assess them. We propose a toolkit (CUBES) of two solutions to help programs arrive at more effective interventions. First, we introduce a novel framework of behavior, which is a practical tool for programs to structure potential drivers and match corresponding interventions. This evidence-based framework was developed through extensive cross-sectoral literature research and refined through application in large-scale global development programs. Second, we propose a set of descriptive, experimental, and simulation approaches that can enhance and expand the methods commonly used in global development. Since not all methods are equally suited to capture the different types of drivers of behavior, we present a decision aid for method selection. We recommend that existing commonly used methods, such as observations and surveys, use CUBES as a scaffold and incorporate validated measures of specific types of drivers in order to comprehensively test all the potential components of a target behavior. We also recommend under-used methods from sectors such as market research, experimental psychology, and decision science, which programs can use to extend their toolkit and test the importance and impact of key enablers and barriers. The CUBES toolkit enables programs across sectors to streamline the process of conceptualizing, designing, and optimizing interventions, and ultimately to change behaviors and achieve targeted outcomes.