Evidence map›Paper›PMID 37964933›Full record

ArticleFrontiers in nutrition2023

Adoption and implementation of produce prescription programs for under-resourced populations: clinic staff perspectives.

Sara C Folta, Zhongyu Li, Sean B Cash, Kurt Hager, Fang Fang Zhang

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Sara C FoltaFriedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States.
Zhongyu LiFriedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States.
Sean B CashFriedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States.
Kurt HagerFriedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States.
Fang Fang ZhangFriedman School of Nutrition Science and Policy, Tufts University, Boston, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Produce prescription programs represent a promising intervention strategy in the healthcare setting to address disparities in diet quality and diet-related chronic disease. The objective of this study was to understand adoption and implementation factors related to these programs that are common across contexts and those that are context-specific. Methods: In this qualitative case comparison study, we conducted qualitative interviews with eight clinic staff from five primary care "safety net" clinics, identified by a partnering non-profit organization that operated the programs, in April-July 2021. Results: Across clinics, the ability to provide a tangible benefit to patients was a key factor in adoption. Flexibility in integrating into clinic workflows was a facilitator of implementation. Fit with usual operations varied across clinics. Common challenges were the need for changes to the workflow and extra staff time. Clinic staff were skeptical about the sustainability of both the benefits to patients and the ability to continue the program at their clinics. Discussion: This study adds to a growing body of knowledge on the adoption and implementation of produce prescription programs. Future research will further this understanding, providing the evidence necessary to guide adopting clinics and to make informed policy decisions to best promote the growth and financial sustainability of these programs.

Indexed as

clinic stafffood as medicineimplementationproduce prescription programsqualitative

Identifiers

PMID37964933
PMCPMC10641783

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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.