A Randomized Controlled Trial of Group Intervention Based on Social Cognitive Theory for Smoking Cessation in China
Bibliographic record
Abstract
BACKGROUND: New training programs need to be developed to help Chinese smokers achieve quitting. The objective of this study was to assess the effectiveness of a group smoking cessation intervention based on social cognitive theory among Chinese smokers. METHOD: A total of 225 smokers were eligible for the study and were randomly assigned to an intervention group (n=118) and a control group (n=107). The intervention group received the course soon after a baseline survey, whereas the control group received routine training in the first 6 months, and then took the same course. Effectiveness was evaluated at 6-month and 1-year follow-up from baseline. RESULTS: After 6 months, 40.5% (47/116) in the intervention group and 5.0% (5/101) in the control group quit smoking (absolute risk reduction: 35.5% [95% confidence interval (CI): 24.2-46.8%]). The 6-month continuous abstinence rate was 28.4% (33/116) in the intervention group and 3.0% (3/101) in the control group (absolute risk reduction 25.4% [95% CI: 15.6-35.2%]). At 1-year follow-up, the proportion of quitting and the 6-month abstinence rate in the intervention group were 35.8% and 22.0%, respectively. The factors associated with smoking cessation during the 6 month period were intervention (adjusted odds ratio [OR]=6.42 [95% CI: 2.46-13.28]), as well as anticipation of quitting (adjusted OR=1.46 [95% CI: 1.12-1.91]) and skill self-efficacy score in the baseline (adjusted OR=1.04 [95% CI: 1.01-1.07]). The same intervention was conducted in the control group after the 6-month study, in which a similar intervention effect was observed. CONCLUSION: A smoking cessation intervention based on social cognitive theory among Chinese smokers is highly effective.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".