Causal Loop Diagramming How-to Guide and Case Study (2023)
收藏DataCite Commons2023-05-17 更新2025-04-16 收录
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https://dataverse.unc.edu/citation?persistentId=doi:10.15139/S3/R7WGID
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资源简介:
Causal Loop Diagramming clearly depicts how multiple factors in a system may interact to cause an outcome (e.g., road traffic crashes), and are used to help better understand and disentangle the complexity surrounding specific problems or issues to inform action planning. This guide and case study are included in a five-part series of systems thinking tools, adapted and applied to support road safety interventions for more effective VZ planning and implementation. Utilizing these resources, stakeholders can establish a firm foundation and deepen their understanding of the system (of policies, norms, funding streams, equitable/inequitable processes, etc.) underlying their road safety outcomes. Systems thinking is an approach to problem-solving and understanding complex problems by analyzing the patterns, dynamics, relationships, and feedback loops among various elements of a system. In these set of resources, researchers from the Collaborative Sciences Center for Road Safety (CSCRS) have developed systems thinking-based content and guidance materials to strengthen the implementation of Vision Zero (VZ) and Safe Systems approaches. These tools and resources introduce the systems thinking framework, and provide guidance on using systems thinking and collaboration tools to support VZ planning and coalition building efforts.
提供机构:
UNC Dataverse
创建时间:
2023-05-11



