Online tobacco websites and online communities-who uses them and do users quit smoking? The quit-primo and national dental practice-based research network Hi-Quit studies
Bibliographic record
Abstract
Online tobacco cessation communities are beneficial but underused. Our study examined whether, among smokers participating in a web-assisted tobacco intervention (Decide2quit.org), specific characteristics were associated with navigating to BecomeAnEx.org, an online cessation community, and with subsequent quit rates. Among smokers (N = 759) registered with Decide2quit.org, we identified visitors to BecomeAnEx.org, examining associations between smoker characteristics and likelihood of visiting. We then tested for associations between visits and 6-month cessation (point prevalence). We also tested for an interaction between use of other online support-seeking (Decide2quit.org tobacco cessation coaches), visiting, and 6-month cessation. One quarter (26.0 %; n = 197) of the smokers visited BecomeAnEx.org; less than one tenth (7.5 %; n = 57) registered to participate in the online forum. Visitors were more likely to be female (73.0 vs. 62.6 % of non-visitors, P < 0.01) to have visited a cessation website before (33.0 vs. 17.4 %, P < 0.01) and to report quit attempts in the previous year (62.0 vs. 53.0 %, P = 0.03). In analyses of all participants, BecomeAnEx.org visiting was not associated with 6-month quit completion. Among participants who communicated with a coach, BecomeAnEx.org visiting also lacked a significant association with 6 month quit completion, although a non-significant trend toward quit completion in visitors was noted (OR 2.21, 95 % CI 0.81-3.1). Online cessation communities attract smokers with previous cessation website experience and recent quit attempts. Community visiting was not associated with quit rates in our study, but low use may have limited our power to detect differences. Further research should explore whether an additive effect can be achieved by offering community visitors support via online coaches.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".