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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.
Afiliación
  • Roberton T; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. timroberton@jhu.edu.
  • Litvin K; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Self A; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
  • Stegmuller AR; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
BMC Public Health ; 17(Suppl 4): 785, 2017 Nov 07.
Article en En | MEDLINE | ID: mdl-29143679
ABSTRACT

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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador / Salud Infantil / Personal Administrativo / Salud Materna / Planificación en Salud Tipo de estudio: Prognostic_studies / Qualitative_research Límite: Child, preschool / Female / Humans / Infant / Newborn / Pregnancy Idioma: En Revista: BMC Public Health Asunto de la revista: SAUDE PUBLICA Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Simulación por Computador / Salud Infantil / Personal Administrativo / Salud Materna / Planificación en Salud Tipo de estudio: Prognostic_studies / Qualitative_research Límite: Child, preschool / Female / Humans / Infant / Newborn / Pregnancy Idioma: En Revista: BMC Public Health Asunto de la revista: SAUDE PUBLICA Año: 2017 Tipo del documento: Article País de afiliación: Estados Unidos
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