Individual-level factors associated with intentions to quit smoking among adult smokers in six cities of China: findings from the ITC China Survey
Bibliographic record
Abstract
BACKGROUND: Over 350 million smokers live in China, and this represents nearly one-third of the smoking population of the world. Smoking cessation is critically needed to help reduce the harms and burden caused by smoking-related diseases. It is therefore important to identify the determinants of quitting and of quit intentions among smokers in China. Such knowledge would have potential to guide future tobacco control policies and programs that could increase quit rates in China. OBJECTIVE: To identify the correlates of intentions to quit smoking among a representative sample of adult smokers in six cities in China. METHODS: Data from wave 1 (2006) of the International Tobacco Control (ITC) Policy Evaluation Project China Survey, a face-to-face survey of adult Chinese smokers in six cities: Beijing, Shenyang, Shanghai, Changsha, Guangzhou and Yinchuan, was analysed. Households were sampled using a stratified multistage design. About 800 smokers were surveyed in each selected city (total n=4815). RESULTS: Past quit attempts, duration of past attempts, Heaviness of Smoking Index (HSI), outcome expectancy of quitting, worry about future health and overall opinion of smoking were found to be independently associated with intentions to quit smoking, but demographic characteristics were not. CONCLUSIONS: The determinants of quit intentions among smokers in China are fairly similar to those found among smokers in Western countries, despite the fact that interest in quitting is considerably lower among Chinese smokers. Identifying the determinants of quit intentions provides possibilities for shaping effective policies and programs for increasing quitting among smokers in China.
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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 | 0.001 | 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".