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All things to all people: trade-offs in pursuit of an ideal modeling tool for maternal and child health

BACKGROUND: Modeling tools have potential to aid decision making for program planning and evaluation at all levels, but are still largely the domain of technical experts, consultants, and global-level staff. One model that can improve decision making for maternal and child health is the Lives Saved...

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Autores principales: Roberton, Timothy, Litvin, Kate, Self, Andrew, Stegmuller, Angela R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688440/
https://www.ncbi.nlm.nih.gov/pubmed/29143679
http://dx.doi.org/10.1186/s12889-017-4751-4
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author Roberton, Timothy
Litvin, Kate
Self, Andrew
Stegmuller, Angela R.
author_facet Roberton, Timothy
Litvin, Kate
Self, Andrew
Stegmuller, Angela R.
author_sort Roberton, Timothy
collection PubMed
description BACKGROUND: Modeling tools have potential to aid decision making for program planning and evaluation at all levels, but are still largely the domain of technical experts, consultants, and global-level staff. One model that can improve decision making for maternal and child health is the Lives Saved Tool (LiST). We examined respondents’ perceptions of LiST’s strengths and weaknesses, to identify ways in which LiST – and similar modeling tools – can adapt to be more accessible and helpful to policy makers. METHODS: We interviewed 21 purposefully sampled LiST users. First, we identified the characteristics that respondents explicitly stated, or implicitly implied, were important in a modeling tool, and then used these results to create a framework for reviewing a modeling tool. Second, we used this framework to categorize the strengths and weaknesses of LiST that respondents articulated. RESULTS: Two overarching qualities were important to respondents: usability and accuracy. For some users, LiST already meets these criteria: it allows for customized input parameters to increase specificity; the interface is intuitive; the assumptions and calculations are scientifically sound; and the standard metric of “additional lives saved” is understood and comparable across settings. Other respondents had different views, although their complaints were typically not that the tool is unusable or inaccurate, but that aspects of the tool could be better explained or easier to understand. CONCLUSION: Government and agency staff at all levels should be empowered to use the data available to them, including the use of models to make full use of these data. For this, we need tools that meet a threshold of both accuracy, so results clarify rather than mislead, and usability, so tools can be used readily and widely, not just by select experts. With these ideals in mind, there are ways in which LiST might continue to be improved or adapted to further advance its uptake and impact. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12889-017-4751-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-56884402017-11-22 All things to all people: trade-offs in pursuit of an ideal modeling tool for maternal and child health Roberton, Timothy Litvin, Kate Self, Andrew Stegmuller, Angela R. BMC Public Health Research BACKGROUND: Modeling tools have potential to aid decision making for program planning and evaluation at all levels, but are still largely the domain of technical experts, consultants, and global-level staff. One model that can improve decision making for maternal and child health is the Lives Saved Tool (LiST). We examined respondents’ perceptions of LiST’s strengths and weaknesses, to identify ways in which LiST – and similar modeling tools – can adapt to be more accessible and helpful to policy makers. METHODS: We interviewed 21 purposefully sampled LiST users. First, we identified the characteristics that respondents explicitly stated, or implicitly implied, were important in a modeling tool, and then used these results to create a framework for reviewing a modeling tool. Second, we used this framework to categorize the strengths and weaknesses of LiST that respondents articulated. RESULTS: Two overarching qualities were important to respondents: usability and accuracy. For some users, LiST already meets these criteria: it allows for customized input parameters to increase specificity; the interface is intuitive; the assumptions and calculations are scientifically sound; and the standard metric of “additional lives saved” is understood and comparable across settings. Other respondents had different views, although their complaints were typically not that the tool is unusable or inaccurate, but that aspects of the tool could be better explained or easier to understand. CONCLUSION: Government and agency staff at all levels should be empowered to use the data available to them, including the use of models to make full use of these data. For this, we need tools that meet a threshold of both accuracy, so results clarify rather than mislead, and usability, so tools can be used readily and widely, not just by select experts. With these ideals in mind, there are ways in which LiST might continue to be improved or adapted to further advance its uptake and impact. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12889-017-4751-4) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-07 /pmc/articles/PMC5688440/ /pubmed/29143679 http://dx.doi.org/10.1186/s12889-017-4751-4 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Roberton, Timothy
Litvin, Kate
Self, Andrew
Stegmuller, Angela R.
All things to all people: trade-offs in pursuit of an ideal modeling tool for maternal and child health
title All things to all people: trade-offs in pursuit of an ideal modeling tool for maternal and child health
title_full All things to all people: trade-offs in pursuit of an ideal modeling tool for maternal and child health
title_fullStr All things to all people: trade-offs in pursuit of an ideal modeling tool for maternal and child health
title_full_unstemmed All things to all people: trade-offs in pursuit of an ideal modeling tool for maternal and child health
title_short All things to all people: trade-offs in pursuit of an ideal modeling tool for maternal and child health
title_sort all things to all people: trade-offs in pursuit of an ideal modeling tool for maternal and child health
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5688440/
https://www.ncbi.nlm.nih.gov/pubmed/29143679
http://dx.doi.org/10.1186/s12889-017-4751-4
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