Quitting smoking in China: findings from the ITC China Survey
Bibliographic record
Abstract
BACKGROUND: Few studies have examined interest in quitting smoking and factors associated with quitting in mainland China. OBJECTIVE: To characterise interest in quitting, quitting behaviour, the use of cessation methods and reasons for thinking about quitting among adult urban smokers in six cities in China. METHODS: Data is from Wave 1 of the ITC China Survey, a face-to-face household survey of 4732 adult smokers randomly selected from six cities in China in 2006. Households were sampled using a stratified multistage design. FINDINGS: The majority of smokers had no plan to quit smoking (75.6%). Over half (52.7%) of respondents had ever tried to quit smoking. Few respondents thought that they could successfully quit smoking (26.5%). Smokers were aware of stop-smoking medications (73.5%) but few had used these medications (5.6%). Only 48.2% had received advice from a physician to quit smoking. The number one reason for thinking about quitting smoking in the last 6 months was concern for personal health (55.0%). Most smokers also believed that the government should do more to control smoking (75.2%). CONCLUSION: These findings demonstrate the need to: (1) increase awareness of the dangers of smoking; (2) provide cessation support for smokers; (3) have physicians encourage smokers to quit; (4) denormalise tobacco use so that smokers feel pressured to quit; (5) implement smoke-free laws to encourage quitting; (6) develop stronger warning labels about the specific dangers of smoking and provide resources for obtaining further cessation assistance; and (7) increase taxes and raise the price of cigarettes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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".