Evidence map›Paper›PMID 41339610›Full record

ArticleInternational journal of behavioral medicine2025

Cancer Screening in Appalachia: A Common-Sense Approach.

Kimberly M Kelly, Sabina O Nduaguba, Randi Shedlosky-Shoemaker, Electra D Paskett, Nancy Schoenberg, Nicole Yantes

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Article in International journal of behavioral medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
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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

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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

5 · Who and what money

Authors and funding

6 authors.

Kimberly M KellyUniversity of Tennessee Health Science Center, Memphis, United States. kkelly44@uthsc.edu.
Sabina O NduagubaWest Virginia University, Morgantown, United States.
Randi Shedlosky-ShoemakerYork College of Pennsylvania, York, United States.
Electra D PaskettThe Ohio State University, Columbus, United States.
Nancy SchoenbergUniversity of Kentucky, Lexington, United States.
Nicole YantesGenesis HealthCare System, Zanesville, United States.

Funding

NCI NIH HHS 2P50CA105632
6 · The paper itself

Abstract

backgroundThe Appalachian region in the USA has some of the highest rates of cancer and cancer mortality in the country, due in part to suboptimal screening. Routine screening for cervical and colorectal cancer can decrease cancer morbidity and mortality. The current study sought to assess the feasibility and preliminary impact of an intervention guided by the Common-sense Model of Self-regulation to improve cervical and colorectal cancer attitudes and screening intentions.

methodParticipants were approached in safety net clinics in Appalachian communities. Individuals completed surveys before and after an intervention that was designed to address lay understandings about the risks and causes of cervical and colorectal cancer and screening. Interventions were conducted with up to three family members, but at least one person in the family was not within screening guidelines. Paired t-tests assessed the impact of the intervention.

resultsParticipants (n = 121) were predominantly women (76.0%) and White (69.4%) and had incomes of $20,000 or less (69.4%). Overall, screening barriers were moderate and did not change after the intervention. In response to the intervention, perceived risk and cervical cancer causes (risk factors) became more accurate, individuals increased in knowledge of colorectal cancer screening, the perceived deadliness of cervical and colorectal cancers decreased, intentions to screen increased, and the appraisal of the effectiveness of screening increased (all p's < 0.05, Holm adjusted).

conclusionThe intervention had a positive impact on lay understanding of cervical and colorectal cancer and increased intentions to have cancer screening. A larger, randomized control trial is merited to investigate the impact of the intervention on screening rates.

Indexed as

Appalachian regionCancer screeningCervical cancerColorectal cancerIntervention, psychosocialModels, psychological

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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.