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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".