Health knowledge and perception of risks among Chinese smokers and non-smokers: findings from the Wave 1 ITC China Survey
Bibliographic record
Abstract
BACKGROUND: Awareness of health risks of smoking is strongly associated with smoking behaviour. However, there are no population-based studies of smoking-related health knowledge in China. OBJECTIVE: The aim of current study was to use a population-based sample from the International Tobacco Control China Wave 1 survey to examine variations between current, former and never smokers' health knowledge about smoking and the impact of health knowledge awareness on smokers' intention to quit. METHODS: A face-to-face interview was conducted with 5986 adult smokers and non-smokers from six cities in China. Respondents were asked whether they believed smoking causes heart disease, stroke, impotence, lung cancer, emphysema, stained teeth, premature ageing in smokers and lung cancer in non-smokers. Current smokers were also asked additional questions on how smoking affects their current and future health as well as whether they had plans to quit smoking and if they believe they would have health benefit from quitting. FINDINGS: The overall awareness of health risks of smoking in China was low compared to developed countries. Current smokers in China were less likely than non-smokers and former smokers to acknowledge the consequences of smoking. Current smokers who were more aware of the health consequences of smoking were more likely to intend to quit smoking. CONCLUSION: These findings highlight the need to increase awareness about the health effects of smoking in China, particularly among current smokers to increase quitting.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".