Evidence map›Paper›PMID 40969635›Full record

ArticleFrontiers in public health2025

Overcoming recruitment barriers through meaningful community engagement: lessons learned from a maternal-child food insecurity assessment in underserved communities.

Gabriela Buccini, Cristina Hernandez, Kristen N Herlosky, Shannon Rabb, Lizette Guillen, Dodds Simangan, Jyoti Desai, Alison Brown, Juanita Chinn, Timothy Grigsby and 2 more

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Observational
  3. 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

12 authors.

Gabriela BucciniSchool of Public Health, University of Nevada, Las Vegas, NV, United States.
Cristina HernandezSchool of Public Health, University of Nevada, Las Vegas, NV, United States.
Kristen N HerloskySchool of Public Health, University of Nevada, Las Vegas, NV, United States.
Shannon RabbNevada Partners Inc., Las Vegas, NV, United States.
Lizette GuillenNevada Partners Inc., Las Vegas, NV, United States.
Dodds SimanganKirk Kerkorian School of Medicine, University of Nevada, Las Vegas, NV, United States.
Jyoti DesaiKirk Kerkorian School of Medicine, University of Nevada, Las Vegas, NV, United States.
Alison BrownNational Institutes of Health, Washington, DC, United States.
Juanita ChinnNational Institutes of Health, Washington, DC, United States.
Timothy GrigsbySchool of Public Health, University of Nevada, Las Vegas, NV, United States.
Jason FlattSchool of Public Health, University of Nevada, Las Vegas, NV, United States.
Ana A BaumannWashington University in Saint Louis, St. Louis, MO, United States.

Funding

Pathways to Integrate Maternal-Child Health and Nutrition Interventions to Transform Equity and Human PotentialU01HD115256 · NICHD · UNIVERSITY OF NEVADA LAS VEGAS · PI BUCCINI, GABRIELA · 2023 to 2025
$2.4M
Pathways to integrate maternal-child health and nutrition interventions to transform equity and human potentialU01OD033239 · OD · UNIVERSITY OF NEVADA LAS VEGAS · PI BUCCINI, GABRIELA · 2021 to 2021
$1.1M
NICHD NIH HHS U01 HD115256NIH HHS U01 OD033239
6 · The paper itself

Abstract

Background: Recruiting participants from underserved communities for research can be challenging due to multilevel barriers. We conducted a maternal-child food insecurity needs assessment within underserved communities in urban Las Vegas, Nevada, and numerous barriers were faced to recruiting participants. This study aimed to examine barriers to participant recruitment, identify practical strategies for engaging participants, and analyze these strategies guided by the Meaningful Community Engagement Framework. Methods: This case study used an ethnography approach to analyze data collection barriers and community engagement strategies employed from March 2022 to February 2023 during a maternal-child food insecurity needs assessment. Inductive and deductive qualitative coding was used to classify barriers across three socio-ecological levels (families, service, and community). Engagement strategies were mapped onto the five principles of the Meaningful Community Engagement Framework guided by social justice, equity, and trust building. Results: Eleven barriers to participant recruitment were identified. At the community level, a history of being over-surveyed created apathy toward the maternal-child food insecurity needs assessment. At the service level, overburdened clinical staff were unwilling to participate in our survey. At the family level, participants questioned the legitimacy of advertising materials, length of the survey, low incentive amount, and were fearful of connection with state or federal programs and mandated reporting. To address the recruitment barriers, fifteen practical engagement strategies were mapped out across principles of the Meaningful Community Engagement Framework: "Building trust and long-term relationships" ( Conclusion: Barriers to participant recruitment emerged across families, service, and community levels, highlighting the systemic challenges to research participation within underserved populations. The successful use of practical engagement strategies leveraged the connection with trusted community organizations and individuals, securing the successful completion of the maternal-child food insecurity needs assessment.

Indexed as

Community ParticipationFood InsecurityFood SupplyPatient SelectionVulnerable PopulationsAdultChildFemaleHumansMaleNeeds AssessmentNevadacommunity based participatory researchengagement strategiesfood insecuritymaternal-childunderserved communities

Identifiers

PMID40969635
PMCPMC12442319

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LicenceCC BY
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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.