A Qualitative Study on Chinese Canadian Male Immigrants’ Perspectives on Stopping Smoking: Implications for Tobacco Control in China
Bibliographic record
Abstract
China has the largest number of smokers in the world; more than half of adult men smoke. Chinese immigrants smoke at lower rates than the mainstream population and other immigrant groups do. This qualitative study was to explore the influence of denormalization in Canada on male Chinese immigrant smoking after migration. Semistructured interviews were conducted with 22 male Chinese Canadian immigrants who were currently smoking or had quit smoking in the past 5 years. The study identified that, while becoming a prospective/father prompted the Chinese smokers to quit or reduce their smoking due to concern of the impacts of their smoking on the health of their young children, changes in smoking were also associated with the smoking environment. Four facilitators were identified which were related to the denomormalized smoking environment in Canada: (a) the stigma related to being a smoker in Canada, (b) conformity with Canadian smoking bans in public places, (c) the reduced social function of smoking in Canadian culture, and (d) the impact of graphic health messages on cigarette packs. Denormalization of tobacco in Canada in combination with collectivist values among Chinese smokers appeared to contribute to participants' reducing and quitting smoking. Although findings of the study cannot be claimed as generalizable to the wider population of Chinese Canadian immigrants due to the small number of the participants, this study provides lessons for the development of tobacco control measures in China to reverse the current prosmoking social environment.
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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 | 0.028 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".