Evaluating Outcomes of a Social Media–Based Peer and Clinician-Supported Smoking Cessation Program in Preventing Smoking Relapse: Mixed Methods Case Study
Bibliographic record
Abstract
BACKGROUND: Smoking relapse prevention after completion of a smoking cessation program is highly germane to reducing smoking rates. OBJECTIVE: The purpose of this study was to evaluate the 1-year outcomes of a social media-based and peer and clinician-supported smoking cessation program on Facebook and examine communication patterns that could support smoking cessation and identify risk of relapse. METHODS: We used a mixed methods case study evaluation approach featuring a single-case holistic design. We recruited volunteers who signed up after successful completion of a 12-week clinical smoking cessation program in a general medicine department in Japan. Participants contemporaneously accessed a closed Facebook page, and we analyzed their posts including text and emoticons. We used joint display analysis, which involved iterative structuring and restructuring construct-specific tables with both types of data to find the most effective approach for integrating the quantitative results with the qualitative results of content analysis. RESULTS: One successful participant and 2 relapsed participants were analyzed to explore the specific patterns of postings prior to relapse. Decisive comments about quitting smoking were common among participants, but encouraging messages for peers were more common from the successful participant. Comments seeking social support and reassurance were warning signs of relapse. Conflicted comments also may be a warning sign of relapse risk. CONCLUSIONS: These findings based on a mixed methods case study of a social media platform supporting smoking cessation could be used to guide messaging in other online social networking service communities after a smoking cessation program to help reduce smoking relapse. TRIAL REGISTRATION: UMIN Clinical Trials Registry UMIN000031172; https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000035595.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".