Global Health Professions Student Survey (GHPSS) in Tobacco Control in China
Bibliographic record
Abstract
OBJECTIVES: To examine the prevalence of smoking, second-hand smoke exposure, and attitudes toward tobacco control and cessation training among university students in China. METHODS: We administered the Global Health Professions Student Survey (GHPSS) to students from 50 universities offering medical/ health professional (MHP) programs and received 11,954 responses. Non-MHP students, MHP students, and third-year MHP students comprised the sample. Descriptive statistics were calculated for weighted prevalence and 95% confidence intervals. Key factors of interest and attitudes toward medical smoking cessation were examined using logistic regression. RESULTS: Smoking and exposure to second-hand smoke was higher among non-MHP (15.9%, 31.9%) than MHP (7.0%, 21.2%) students. For third-year MHP students, the rates were 6.9% and 19.7%, respectively. Students held positive attitudes toward smoking bans in public places and cessation services. However, few received formal training in smoking cessation, and 37.0% agreed that light cigarettes are less harmful to health. Positive attitudes toward cessation services were related to several factors. CONCLUSIONS: This study is the first comprehensive survey of students in China providing direction for building capacity in tobacco control and smoking cessation among students in health professional programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".