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Record W4389086956 · doi:10.2196/52398

A Christian Faith-Based Facebook Intervention for Smoking Cessation in Rural Communities (FAITH-CORE): Protocol for a Community Participatory Development Study

2023· article· en· W4389086956 on OpenAlexvenueno aff
Pravesh Sharma, Brianna Tranby, Celia Kamath, Tabetha A. Brockman, Anne I. Roche, Christopher J. Hammond, LaPrincess C. Brewer, Pamela S. Sinicrope, N. Kay Lenhart, Brian Quade, Nate Abuan, Martin Halom, J. Erin Staples, Christi A. Patten

Bibliographic record

VenueJMIR Research Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesCenter for Clinical and Translational Science, Mayo Clinic
KeywordsSmoking cessationQuitlineMedicinePsychological interventionCommunity-based participatory researchIntervention (counseling)Participatory action researchEnvironmental healthNursingEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: Tobacco smoking remains the leading cause of preventable morbidity and mortality in the United States, with significant rural-urban disparities. Adults who live in rural areas of the United States have among the highest tobacco smoking rates in the nation and experience a higher prevalence of smoking-related deaths and deaths due to chronic diseases for which smoking is a causal risk factor. Barriers to accessing tobacco use cessation treatments are a major contributing factor to these disparities. Adults living in rural areas experience difficulty accessing tobacco cessation services due to geographical challenges, lack of insurance coverage, and lack of health care providers who treat tobacco use disorders. The use of digital technology could be a practical answer to these barriers. OBJECTIVE: This report describes a protocol for a study whose main objectives are to develop and beta test an innovative intervention that uses a private, moderated Facebook group platform to deliver peer support and faith-based cessation messaging to enhance the reach and uptake of existing evidence-based smoking cessation treatment (EBCT) resources (eg, state quitline coaching programs) for rural adults who smoke. METHODS: We will use the Integrated Theory of Health Behavior Change, surface or deep structure frameworks to guide intervention development, and the community-based participatory research (CBPR) approach to identify and engage with community stakeholders. The initial content library of moderator postings (videos and text or image postings) will be developed using existing EBCT material from the Centers for Disease Control and Prevention Tips from Former Smokers Campaign. The content library will feature topics related to quitting smoking, such as coping with cravings and withdrawal and using EBCTs with faith-based message integration (eg, Bible quotes). A community advisory board and a community engagement studio will provide feedback to refine the content library. We will also conduct a beta test of the intervention with 15 rural adults who smoke to assess the recruitment feasibility and preliminary intervention uptake such as engagement, ease of use, usefulness, and satisfaction to further refine the intervention based on participant feedback. RESULTS: The result of this study will create an intervention prototype that will be used for a future randomized controlled trial. CONCLUSIONS: Our CBPR project will create a prototype of a Facebook-delivered faith-based messaging and peer support intervention that may assist rural adults who smoke to use EBCT. This study is crucial in establishing a self-sufficient smoking cessation program for the rural community. The project is unique in using a moderated social media platform providing peer support and culturally relevant faith-based content to encourage adult people who smoke to seek treatment and quit smoking. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52398.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.036
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.065
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.026
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.002
Science and technology studies0.0050.003
Scholarly communication0.0030.003
Open science0.0030.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0650.010

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.648
GPT teacher head0.612
Teacher spread0.036 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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