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Modeling Outputs Can Be Valuable When Uncertainty Is Appropriately Acknowledged, but Misleading When Not

While modeling approaches seek to draw on the best available evidence to project health impact of improved coverage of specific interventions, uncertainty around the outputs often remains. When the modeling estimates are used for advocacy, these uncertainties should be communicated to policy makers...

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Detalles Bibliográficos
Autor principal: Hodgins, Steve
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Global Health: Science and Practice 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5752600/
https://www.ncbi.nlm.nih.gov/pubmed/29284692
http://dx.doi.org/10.9745/GHSP-D-17-00444
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author Hodgins, Steve
author_facet Hodgins, Steve
author_sort Hodgins, Steve
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description While modeling approaches seek to draw on the best available evidence to project health impact of improved coverage of specific interventions, uncertainty around the outputs often remains. When the modeling estimates are used for advocacy, these uncertainties should be communicated to policy makers clearly and openly to ensure they understand the model's limits and to maintain their confidence in the process.
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spelling pubmed-57526002018-01-10 Modeling Outputs Can Be Valuable When Uncertainty Is Appropriately Acknowledged, but Misleading When Not Hodgins, Steve Glob Health Sci Pract Editorial While modeling approaches seek to draw on the best available evidence to project health impact of improved coverage of specific interventions, uncertainty around the outputs often remains. When the modeling estimates are used for advocacy, these uncertainties should be communicated to policy makers clearly and openly to ensure they understand the model's limits and to maintain their confidence in the process. Global Health: Science and Practice 2017-12-28 /pmc/articles/PMC5752600/ /pubmed/29284692 http://dx.doi.org/10.9745/GHSP-D-17-00444 Text en © Hodgins. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited. To view a copy of the license, visit http://creativecommons.org/licenses/by/4.0/. When linking to this article, please use the following permanent link: https://doi.org/10.9745/GHSP-D-17-00444
spellingShingle Editorial
Hodgins, Steve
Modeling Outputs Can Be Valuable When Uncertainty Is Appropriately Acknowledged, but Misleading When Not
title Modeling Outputs Can Be Valuable When Uncertainty Is Appropriately Acknowledged, but Misleading When Not
title_full Modeling Outputs Can Be Valuable When Uncertainty Is Appropriately Acknowledged, but Misleading When Not
title_fullStr Modeling Outputs Can Be Valuable When Uncertainty Is Appropriately Acknowledged, but Misleading When Not
title_full_unstemmed Modeling Outputs Can Be Valuable When Uncertainty Is Appropriately Acknowledged, but Misleading When Not
title_short Modeling Outputs Can Be Valuable When Uncertainty Is Appropriately Acknowledged, but Misleading When Not
title_sort modeling outputs can be valuable when uncertainty is appropriately acknowledged, but misleading when not
topic Editorial
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5752600/
https://www.ncbi.nlm.nih.gov/pubmed/29284692
http://dx.doi.org/10.9745/GHSP-D-17-00444
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