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• W3217389326 abstract "The United States has been unsuccessful in containing the rapid spread of COVID-19. The complex epidemiology of the disease and the fragmented response to it has resulted in thousands of ways in which spread has occurred, creating a situation where each community needs to create its own local, context-specific learning model while remaining compliant to county or state mandates.In this paper, we demonstrate how cross sector collaborations can use the Cynefin Framework, a tool for decision-making in complex systems, to guide community response to the COVID-19 pandemic.We explore circumstances under which communities can inhabit each of the four domains of systems complexity represented in the Cynefin framework: simple, complicated, chaotic, and complex, and describe the decision-making process in each domain that balances health, economic, and social well-being.This paper serves as a call to action for the creation of community learning systems to improve community resilience and capacity to make better-informed decisions to address complex public health problems during the pandemic and beyond." @default.
• W3217389326 created "2021-12-06" @default.
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• W3217389326 date "2021-11-26" @default.
• W3217389326 modified "2023-09-24" @default.
• W3217389326 title "A learning approach to community response during the <scp>COVID</scp>‐19 pandemic: Applying the Cynefin framework to guide decision‐making" @default.
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• W3217389326 doi "https://doi.org/10.1002/lrh2.10295" @default.
• W3217389326 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35434354" @default.
• W3217389326 hasPublicationYear "2021" @default.
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