Evidence map›Paper›PMID 36530206›Full record

ArticleAgriculture and human values2022

The COVID-19 pandemic and food assistance organizations' responses in New York's Capital District.

Lauren Winkler, Taylor Goodell, Siddharth Nizamuddin, Sam Blumenthal, Nurcan Atalan-Helicke

Abstract read
In one paragraph

Article in Agriculture and human values, 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
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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.

Lauren WinklerBoston, USA.
Taylor GoodellAlbany, USA.
Siddharth NizamuddinLong Beach, USA.
Sam BlumenthalBoston, USA.
Nurcan Atalan-HelickeSkidmore College Environmental Studies and Sciences Program, 815 North Broadway, Saratoga Springs, NY 12866 USA.ORCID 0000-0002-4365-4907

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This research examines the impact of COVID-19 on food security in New York state and the innovative approaches employed by food assistance organizations to help address the changing and increasing demand for their services from March 2020 to May 2021. We examine the case study of New York's Capital District region through a qualitative approach. We find that there was a sharp increase in utilization of emergency services during spring of 2020, which tapered off in the summer and fall of 2020 but remained above the levels of need seen the previous year. Food assistance organizations quickly adapted to the increased demand for their services and changing conditions to reduce gaps in local food distribution chains: They reorganized and tapped into new sources for volunteers, networked with public and private organizations, and coordinated work with other regional food pantries for maximum impact. The flexibility of food assistance organizations to address the disruptions brought about by the COVID-19 pandemic highlights their critical roles in the U.S. food security environment. While organizations are aware of their shortcomings, constraints, and overall role in the American food system, the majority also expressed that the pandemic presented an opportunity to treat a complex problem together and to enact change. Several stakeholders also shared their hope that strengthening their networks and innovations may facilitate post-pandemic recovery, bring about systemic changes to address root causes of food insecurity, and better serve the communities most vulnerable to hunger and service disruptions.

Indexed as

COVID-19Food PantriesFood SecurityInnovationNew York Capital District

Identifiers

PMID36530206
PMCPMC9734295

What OpenQuestion holds

Textmetadata
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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.