Achieving the Goals of Healthy China 2030 Depends on Increasing Smoking Cessation in China: Comparative Findings from the ITC Project in China, Japan, and the Republic of Korea
Bibliographic record
Abstract
Tobacco smoking is the number one preventable cause of disease and death in China as it is globally. Indeed, the toll of smoking in China is much greater than its status as the world's most populous country. There is a persistent and continuing need for China to implement the measures specified in the global tobacco control treaty, the World Health Organization (WHO) Framework Convention on Tobacco Control (FCTC), which China ratified in 2005. The theme for the 2021 WHO World No Tobacco Day focuses on the need to support smoking cessation. This article presents findings from the International Tobacco Control (ITC) Policy Evaluation Project cohort surveys in China, in comparison to ITC cohort surveys in two neighboring countries: Japan and the Republic of Korea. These findings demonstrate that smokers in China very much want to quit, but these intentions are not being translated into quit attempts, relative to smokers in Japan and the Republic of Korea. Additionally, about 80% of Chinese smokers want the Chinese government to do more to control smoking. These findings reaffirm the need for China to implement strong, evidence-based measures to reduce smoking. The objective of Healthy China 2030 to reduce deaths from non-communicable diseases by 30% can be achieved by reducing smoking prevalence from its current 26.6% to 20%, and this reduction can be achieved through strong implementation of FCTC measures.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".