Evidence map›Paper›PMID 41393039›Full record

ArticleFrontiers in public health2025

Community case study: Community-driven strategies to build a socially inclusive rural county.

Elizabeth Ablah, Nadine Long, Christina M Pacheco, Maddi Sutter, Shelby Bohnert, Sharon Fitzgerald Wolff, Sarah Finocchario-Kessler

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. Not yet cited in PubMed.

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

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

7 authors.

Elizabeth AblahDepartment of Population Health, University of Kansas School of Medicine-Wichita, Wichita, KS, United States.
Nadine LongSauerwein-Long Consulting, LLC, Andover, KS, United States.
Christina M PachecoDepartment of Family Medicine, University of Kansas Medical Center, Kansas City, KS, United States.
Maddi SutterMitchell County Local Health Equity Team, Beloit, KS, United States.
Shelby BohnertMitchell County Regional Medical Foundation, Beloit, KS, United States.
Sharon Fitzgerald WolffDepartment of Population Health, University of Kansas Medical Center, Kansas City, KS, United States.
Sarah Finocchario-KesslerDepartment of Family Medicine, University of Kansas Medical Center, Kansas City, KS, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mitchell County, Kansas is a rural county with a strong sense of local identity and a population committed to community well-being. Like many rural areas, however, residents experience structural challenges to social connection, including geographic isolation, demographic shifts, and limited access to inclusive services. These issues were further exacerbated by the COVID-19 pandemic. Through a federally funded initiative, Communities Organizing to Promote Equity (COPE), Mitchell County stakeholders developed community-driven strategies to address social isolation, with special consideration on how to include new immigrant residents. This case study outlines the intervention, centered on engagement and trust-building, cultural inclusion, employment access, and wrap-around supports; highlighting how other rural communities might replicate this work to build more welcoming, connected, and inclusive environments.

Indexed as

Community ParticipationCOVID-19Rural PopulationSocial IsolationEmigrants and ImmigrantsHumansKansasOrganizational Case StudiesSARS-CoV-2community-drivenparticipatoryruralsocial inclusionsocial isolation

Identifiers

PMID41393039
PMCPMC12695761

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.