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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Global Health: Science and Practice
2017
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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 |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-5752600 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Global Health: Science and Practice |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT hodginssteve modelingoutputscanbevaluablewhenuncertaintyisappropriatelyacknowledgedbutmisleadingwhennot |