Urban Chinese Smokers From Lower Socioeconomic Backgrounds Face More Barriers to Quitting: Results From the International Tobacco Control-China Survey
Bibliographic record
Abstract
INTRODUCTION: Research findings on social disparities in barriers to quitting faced by smokers from mainly Western English-language countries may or may not generalize to smokers in China. This paper sought to determine whether nicotine dependence, quitting self-efficacy, quitting interest differ by socio-economic status (SES), and whether they mediate the relationship between SES and quitting behavior of urban Chinese smokers. METHODS: Data come from 7,309 adult smokers who participated in the first 3 waves of the International Tobacco Control-China survey conducted in 7 cities across China. The association of socio-economic indicators with nicotine dependence, quitting self-efficacy, quitting interest, and behavior was evaluated using generalized estimating equations models along with a formal test of mediational effects. RESULTS: The SES index indicated that those from lower SES were significantly more addicted (p < .001), less confident (p < .001), and less interested in quitting (p < .05). This finding was replicated by education and employment status, but it was not clearly related to income. Mediational analyses revealed that the effects of SES on making quit attempts and quit success among those who tried were indirect. For quit attempts, self-efficacy, interest to quit, and heaviness of smoking index (HSI) were all significant mediators of the SES effect (p < .001), but for maintenance, only HSI was a significant mediator (p < .001). CONCLUSIONS: Urban Chinese smokers from lower socio- economic backgrounds experience greater levels of psychological and behavioral barriers to quitting than their counterparts from higher socio-economic backgrounds and as such, they need more help to quit and do so successfully.
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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 | 0.000 |
| 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".