Behavioural interventions for smoking cessation: a meta-analysis of randomized controlled trials
Bibliographic record
Abstract
AIMS: Widely varying estimates of treatment effects have been reported in randomized controlled trials (RCTs) investigating the efficacy of behavioural interventions for smoking cessation. Previous meta-analyses investigating behavioural interventions have important limitations and do not include recently published RCTs. We undertook a meta-analysis of RCTs to synthesize the treatment effects of four behavioural interventions, including minimal clinical intervention (brief advice from a healthcare worker), and intensive interventions, including individual, group, and telephone counselling. METHODS AND RESULTS: We searched the CDC Tobacco Information and Prevention, Cochrane Library, EMBASE, Medline, and PsycINFO databases. We included only RCTs that reported biochemically validated smoking cessation outcomes at 6 and/or 12 months after the target quit date. Outcomes were aggregated using hierarchical Bayesian random-effects models. We identified 50 RCTs, which randomized n = 26 927 patients (minimal clinical intervention: 9 RCTs, n = 6456; individual counselling: 23 RCTs, n = 8646; group counselling: 12 RCTs, n = 3600; telephone counselling: 10 RCTs, n = 8225). The estimated mean treatment effects were minimal clinical intervention [odds ratio (OR) 1.50, 95% credible interval (CrI) 0.84-2.78], individual counselling (OR 1.49, 95% CrI 1.08-2.07), group counselling (OR 1.76, 95% CrI 1.11-2.93), and telephone counselling (OR 1.58, 95% CrI 1.15-2.29). CONCLUSION: Intensive behavioural interventions result in substantial increases in smoking abstinence compared with control. Although minimal clinical intervention may increase smoking abstinence, there is insufficient evidence to draw strong conclusions regarding its efficacy.
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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 | 0.060 | 0.137 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.035 | 0.062 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".