A learning approach to community response during the COVID-19 pandemic: Applying the Cynefin framework to guide decision-making.
Learn Health Syst
; 6(2): e10295, 2022 Apr.
Article
em En
| MEDLINE
| ID: mdl-35434354
ABSTRACT
Introduction:
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.Methods:
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.Results:
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.Conclusion:
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Learn Health Syst
Ano de publicação:
2022
Tipo de documento:
Article