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Relationship-building around a policy decision-support tool for urban health

Contemporary challenges linked to public health and climate change demand more effective decision-making and urban planning practices, in particular by taking greater account of evidence. In order to do this, trust-building relationships between scientists and urban practitioners through collaborati...

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Detalles Bibliográficos
Autores principales: Deloly, Clément, Gall, Anne Roué-Le, Moore, Gemma, Bretelle, Lucy, Milner, James, Mohajeri, Nahid, Osrin, David, Salvia, Giuseppe, Symonds, Phil, Tsoulou, Ioanna, Zimmermann, Nici, Wilkinson, Paul, Davies, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7611888/
https://www.ncbi.nlm.nih.gov/pubmed/34704038
http://dx.doi.org/10.5334/bc.110
Descripción
Sumario:Contemporary challenges linked to public health and climate change demand more effective decision-making and urban planning practices, in particular by taking greater account of evidence. In order to do this, trust-building relationships between scientists and urban practitioners through collaborative research programmes is required. Based on a policy-relevant research project, Complex Urban Systems for Sustainability and Health (CUSSH), this project aims to support the transformation of cities to meet environmental imperatives and to improve health with a quantitative health impact assessment. A case study in Rennes, France, focuses on the role of a policy decision-support tool in the production and use of knowledge to support evidence-informed decision-making. Although the primary objective of informing decision-making through evidence-based science is not fulfilled, the use of a decision-making support tool can lay the foundations for relationship-building. It can serve as a support for boundary-spanning activities, which are recognised for their effectiveness in linking science to action. This case study illustrates that the path of knowledge transfer from science to policy can be challenging, and the usefulness of using models may not be where it was thought to have been.