Tobacco Smoking Among Migrant Factory Workers in Shenzhen, China
Bibliographic record
Abstract
BACKGROUND: While several studies of smoking behaviors in rural-to-urban Chinese migrants exist, none to our knowledge have focused on factory workers, estimated to represent between 10% and 20% of China's total rural-to-urban migratory population. This paper assesses factors associated with smoking behavior among rural-to-urban migrant factory workers in Shenzhen, China. METHODS: A cross-sectional survey of migrant workers from 44 randomly selected factories in Shenzhen, China. Participants were migrant factory workers aged 16-59 years and holding nonlocal household registration. The main outcome measures were demographic, migration-related, and behavioral factors associated with smoking status. RESULTS: Four thousand and eighty-eight completed questionnaires were obtained (response rate 95.5%). Overall smoking prevalence (including occasional, daily, and heavy daily smoking) was 19.1%. The prevalence of daily smoking (including heavy daily smoking) was higher in men (27.3%) than women (0.7%). These rates are significantly lower than national smoking rates (59.5% in men, 3.7% in women) and rates found in a similar study. A high-risk group of men who smoke heavily and consume alcohol frequently was identified. Longer working hours and less rest were associated with higher rates of smoking. Frequent Internet use and lack of insurance were associated with lifetime smoking. Gender-adjusted models showed that poorer mental health and an accumulated working time in Shenzhen of 2-3 years increased female workers' likelihood of smoking. CONCLUSIONS: Migrant factory workers in Shenzhen had lower rates of smoking than other population groups in China. The identification of risk factors for heavy smoking may help to effectively target health promotion interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".