Susceptibility to smoking and determinants among medicalstudents: A representative nationwide study in China
Bibliographic record
Abstract
INTRODUCTION: The rationale behind why the majority of medical students are non-smokers, but some initiate smoking after becoming physicians is not fully understood in China. Exploring factors that may increase susceptibility to smoking initiation among medical students is an essential first step in assessing preventative actions. METHODS: Participants were 11954 students, who were identified through a multistage survey sampling process that included 50 universities in China. Subsequent analysis focused on 8916 non-smokers among medical students. Both unadjusted and adjusted logistic methods were considered in the data analyses. RESULTS: The prevalence of susceptibility to smoking was 23.0%. Multivariate logistic regression analyses found that exposure to secondhand smoke (SHS) in domestic places (OR= 1.63) and in public places (OR=1.78), cigarette advertising (OR=1.91) and promotional activities on campus (OR=1.90) were positively associated with susceptibility to smoking. In contrast, positive attitudes toward tobacco control on the part of health professionals, HPs, (OR=0.52) were negatively associated with susceptibility to smoking. Those who received information about the dangers of smoking (OR=0.75) and did not agree that light cigarettes are less harmful to health (OR=0.79) were less susceptible to smoke. Caring about exposure to secondhand smoke (OR=0.68 care, and OR=0.33 very) and advising family members to stop smoking (OR=0.81) were negatively associated with susceptibility to smoking. CONCLUSIONS: These findings underscore the importance of tobacco control training and establishing smoke-free campuses for reducing susceptibility to smoking among medical students.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".