ArticleGates open research2019
CUBES: A practical toolkit to measure enablers and barriers to behavior for effective intervention design.
Article in Gates open research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Implementation practice models for development in low- and middle-income countries: systematic review of peer-reviewed literature.BMC public health · 2022Pooled it
- Article
- Discovery of interconnected causal drivers of COVID-19 vaccination intentions in the US using a causal Bayesian network.Scientific reports · 2023Article
- Women's contraceptive profiles in Burundi: Knowledge, attitudes, and interactions with media and health services.PloS one · 2022Article
- Community health workers at the dawn of a new era: 10. Programme performance and its assessment.Health research policy and systems · 2021Review
- Understanding drivers of family planning in rural northern India: An integrated mixed-methods approach.PloS one · 2021Article
- Closing the gap on institutional delivery in northern India: a case study of how integrated machine learning approaches can enable precision public health.BMJ global health · 2020Article
- Bringing Greater Precision to Interactions Between Community Health Workers and Households to Improve Maternal and Newborn Health Outcomes in India.Global health, science and practice · 2020Article
- Understanding why at-risk population segments do not seek care for tuberculosis: a precision public health approach in South India.BMJ global health · 2020Article
- Identifying population segments for effective intervention design and targeting using unsupervised machine learning: an end-to-end guide.Gates open research · 2019Article
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Authors and funding
2 authors.
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Abstract
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.
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