The use of cessation assistance among smokers from China: Findings from the ITC China Survey
Bibliographic record
Abstract
BACKGROUND: Stop smoking medications significantly increase the likelihood of smoking cessation. However, there are no population-based studies of stop-smoking medication use in China, the largest tobacco market in the world. This study examined stop-smoking medication use and its association with quitting behavior among a population-based sample of Chinese smokers. METHODS: Face-to-face interviews were conducted with 4,627 smokers from six cities in the ITC China cohort survey. Longitudinal analyses were conducted using Wave 1 (April to August, 2006) and Wave 2 (November 2007 to January 2008). RESULTS: Approximately 26% of smokers had attempted to quit between Waves 1 and 2, and 6% were abstinent at 18-month follow-up. Only 5.8% of those attempting to quit reported NRT use and NRT was associated with lower odds of abstinence at Wave 2 (OR = 0.11; 95%CI = 0.03-0.46). Visiting a doctor/health professional was associated with greater attempts to quit smoking (OR = 1.60 and 2.78; 95%CI = 1.22-2.10 and 2.21-3.49 respectively) and being abstinent (OR = 1.77 and 1.85; 95%CI = 1.18-2.66 and 1.13-3.04 respectively) at 18-month follow-up relative to the smokers who did not visit doctor/health professional. CONCLUSIONS: The use of formal help for smoking cessation is low in China. There is an urgent need to explore the use and effectiveness of stop-smoking medications in China and in other non-Western markets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".