Evidence map›Paper›PMID 35443074›Full record

ArticleAnnals of the New York Academy of Sciences2022

Nutrition modeling tools: a qualitative study of influence on policy decision making and determining factors.

Frances Knight, Megan W Bourassa, Elaine Ferguson, Helen Walls, Saskia de Pee, Stephen Vosti, Homero Martinez, Carol Levin, Monica Woldt, Kavita Sethurman and 1 more

Abstract read
In one paragraph

Article in Annals of the New York Academy of Sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Frances KnightDepartment of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Megan W BourassaNew York Academy of Sciences, New York, New York.ORCID 0000-0003-1201-3397
Elaine FergusonDepartment of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0003-4673-5128
Helen WallsDepartment of Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Saskia de PeeNutrition Division, United Nations World Food Programme, Rome, Italy.
Stephen VostiDepartment of Agricultural and Resource Economics, University of California, Davis, Davis, California.
Homero MartinezNutrition International, Ottawa, Ontario, Canada.ORCID 0000-0002-1435-0306
Carol LevinDepartment of Global Health, University of Washington, Seattle, Washington.
Monica WoldtHelen Keller International, Washington, District of Columbia.
Kavita SethurmanFormerly with Food and Nutrition Technical Assistance Project (FANTA), Washington, District of Columbia.
Gilles BergeronNew York Academy of Sciences, New York, New York.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nutrition modeling tools (NMTs) generate evidence to inform policy and program decision making; however, the literature is generally limited to modeling methods and results, rather than use cases and their impacts. We aimed to document the policy influences of 12 NMTs and identify factors influencing them. We conducted semistructured interviews with 109 informants from 30 low- and middle-income country case studies and used thematic analysis to understand the data. NMTs were mostly applied by international organizations to inform national government decision making. NMT applications contributed to enabling environments for nutrition and influenced program design and policy in most cases; however, this influence could be strengthened. Influence was shaped by processes for applying the NMTs; ownership of the analysis and data inputs, and capacity building in NMT methods, encouraged uptake. Targeting evidence generation at specific policy cycle stages promoted uptake; however, where advocacy capacity allowed, modeling was embedded ad hoc into emerging policy discussions and had broader influence. Meanwhile, external factors, such as political change and resource constraints of local partner organizations, challenged NMT implementation. Importantly, policy uptake was never the result of NMTs exclusively, indicating they should be nested persistently and strategically within the wider evidence and advocacy continuum, rather than being stand-alone activities.

Indexed as

Health PolicyPolicy MakingCapacity BuildingDecision MakingHumansNutritional StatusNutrition Policydecision makerslinear programmingnutrition modeling toolspolicy-making processes

Identifiers

PMID35443074
PMCPMC9546113

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.