Evidence map›Paper›PMID 35368735›Full record

ArticleCurrent developments in nutrition2022

Successful Community Nutrition Incentive Program Data Collection during the COVID-19 Pandemic: A Case Study.

Sarah A Stotz, Hollyanne Fricke, Cameron Perra, Carmen Byker-Shanks, Amy L Yaroch

Abstract read
In one paragraph

Article in Current developments in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Sarah A StotzCenters for American Indian and Alaska Native Health, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.ORCID https://orcid.org/0000-0003-4107-3313
Hollyanne FrickeGretchen Swanson Center for Nutrition, Omaha, NE, USA.
Cameron PerraHunger Task Force, Milwaukee, WI, USA.
Carmen Byker-ShanksGretchen Swanson Center for Nutrition, Omaha, NE, USA.
Amy L YarochGretchen Swanson Center for Nutrition, Omaha, NE, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The coronavirus 2019 (COVID-19) pandemic has complicated rigorous evaluation of public health nutrition programs. The USDA Gus Schumacher Nutrition Incentive Program (USDA GusNIP) funds nutrition incentive programs to improve fruit and vegetable purchasing and intake by incentivizing Supplemental Nutrition Assistance Program (SNAP) participants at the point of sale. GusNIP grantees are required to collect survey data (e.g., fruit and vegetable intake and food insecurity status) on a subset of participants. However, due to COVID-19, most GusNIP grantees faced formidable barriers to data collection. The Hunger Task Force Mobile Market (HTFMM), a Wisconsin-based 2019 GusNIP grantee, used particularly innovative methods to successfully collect these data ( Objectives: The aim was to explore HTFMM's successful participant-level data-collection evaluation during COVID-19. Methods: A single case study methodological approach framed this study. The case is the HTFMM in Milwaukee, WI, USA. Participants included HTFMM leadership ( Results: Four salient themes emerged: Conclusions: These findings provide guidance on how alternative methods for data collection during COVID-19 can be used and applied to other situations that may affect the ability to collect participant-level data. These findings contribute to a growing body of literature as to best practices and approaches to collecting participant-level data to evaluate public health nutrition programs.

Indexed as

COVID-19fruit and vegetable consumptionnutrition incentiveprogram evaluationqualitative

Identifiers

PMID35368735
PMCPMC8903743

What OpenQuestion holds

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Registered trials

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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.