Beta Test of a Christian Faith-Based Facebook Intervention for Smoking Cessation in Rural Communities (FaithCore): Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Individuals living in rural communities experience substantial geographic and infrastructure barriers to attaining health equity in accessing tobacco use cessation treatment. Social media and other digital platforms offer promising avenues to improve access and overcome engagement challenges in tobacco cessation efforts. Research has also shown a positive correlation between faith-based involvement and a lower likelihood of smoking, which can be used to engage rural communities in these interventions. OBJECTIVE: This study aimed to develop and beta test a social intervention prototype using a Facebook (Meta Platforms, Inc) group specifically designed for rural smokers seeking evidence-based smoking cessation resources. METHODS: We designed a culturally aligned and faith-aligned Facebook group intervention, FaithCore, tailored to engage rural people who smoke in smoking cessation resources. Both intervention content and engagement strategies were guided by community-based participatory research principles. Given the intervention's focus on end users, that is, rural people who smoked, we conducted a beta test to assess any technical or usability issues of this intervention before any future trials for large-scale implementation. RESULTS: No critical beta test technical and usability issues were noted. Besides, the FaithCore intervention was helpful, easy to understand, and achieved its intended goals. Notably, 90% (9/10) of the participants reported that they tried quitting smoking, while 90% (9/10) reported using or seeking cessation resources discussed within the group. CONCLUSIONS: This study shows that social media platform with culturally aligned and faith-aligned content and engagement strategies delivered by trained moderators are promising for smoking cessation interventions in rural communities. Our future step is to conduct a large pilot trial to evaluate the intervention's effectiveness on smoking cessation outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".