Internet-Based Intervention Compared to Brief Intervention for Smoking Cessation in Brazil: Pilot Study
Bibliographic record
Abstract
BACKGROUND: Smoking is still the leading cause of preventable death. Governments and health care providers should make available more accessible resources to help tobacco users stop. OBJECTIVE: This study describes a pilot longitudinal study that evaluated the efficacy of an internet-based intervention compared to the brief intervention for smoking cessation among Brazilians. METHODS: Eligible participants were recruited and randomly allocated to one of the two interventions. Measures were drawn by comparing cessation rates, motivation scores, and sought treatment between groups, assessed 1 and 3 months after the intervention. Inferential analysis was performed to compare the participants' characteristics, and the intention to treat was calculated. RESULTS: A total of 49 smokers were enrolled in this study (n=25, 51% in the brief intervention group; n=24, 49% in the internet-based intervention group). Mean age was 44.5 (SD 13.3) years; most were male (n=29, 59.2%), had elementary school (n=22, 44.9%), smoked 14.5 cigarettes per day on average (SD 8.6), and had a mean score of 4.65 for nicotine dependence and 5.7 for motivation to quit. Moreover, 35 (71%) participants answered follow-up 1, and 19 (39%) answered follow-up 2. The results showed similar rates of cessation and reduction for both intervention groups. CONCLUSIONS: The internet-based intervention was slightly more effective for smoking cessation, while the brief intervention was more effective in reducing the number of cigarettes smoked per day. This difference was small and had no statistical significance even after adjusting for intention-to-treat analysis. These results should be interpreted with caution, especially due to the small sample size.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".