Effect of a Digital Social Media Campaign on Young Adult Smoking Cessation
Bibliographic record
Abstract
INTRODUCTION: Social media (SM) may extend the reach and impact for smoking cessation among young adult smokers. To-date, little research targeting young adults has been done on the use of SM to promote quitting smoking. We assessed the effect of an innovative multicomponent web-based and SM approach known as Break-it-Off (BIO) on young adult smoking cessation. METHODS: The study employed a quasi-experimental design with baseline and 3-month follow-up data from 19 to 29-year old smokers exposed to BIO (n = 102 at follow-up) and a comparison group of Smokers' Helpline (SHL) users (n = 136 at follow-up). Logistic regression analysis assessed differences between groups on self-reported 7-day and 30-day point prevalence cessation rates, adjusting for ethnicity, education level, and cigarette use (daily or occasional) at baseline. RESULTS: The campaign reached 37 325 unique visitors with a total of 44 172 visits. BIO users had significantly higher 7-day and 30-day quit rates compared with users of SHL. At 3-month follow-up, BIO participants (32.4%) were more likely than SHL participants (14%) to have quit smoking for 30 days (odds ratio = 2.95, 95% CI = 1.56 to 5.57, P < .001) and BIO participants (91%) were more likely than SHL participants (79%) to have made a quit attempt (odds ratio = 2.69, 95% CI = 1.03 to 6.99, P = .04). CONCLUSION: The reach of the campaign and findings on quitting success indicate that a digital/SM platform can complement the traditional SHL cessation service for young adult smokers seeking help to quit.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".