A qualitative study of Chinese Canadian fathers’ smoking behaviors: intersecting cultures and masculinities
Bibliographic record
Abstract
BACKGROUND: China is home to the largest number of smokers in the world; more than half of the male population smoke. Given the high rates of Chinese immigration to Canada and the USA, researchers have explored the effect of immigration on Chinese smokers. Reduced tobacco use among Chinese immigrants has been reported in the United States; however, little is known about the social factors underlying men's smoking practices in settings where tobacco control measures have denormalized smoking, and in the context of fatherhood. The purpose of this Canada-based study was to explore the smoking-related experiences of immigrant Chinese fathers. METHODS: In this qualitative study, semi-structured telephone interviews were conducted with 22 Chinese Canadian fathers who smoked or had recently quit smoking, and had at least one child under the age of five years old. RESULTS: The Chinese fathers had dramatically changed their smoking patterns due to concern for their children's health and social norms and restrictions related to smoking in Canada. The facilitators and barriers for men's smoking were intertwined with idealized masculine provider and protector roles, and diverse Canadian Chinese cultural norms related to tobacco use. CONCLUSIONS: The findings have implications for the development of future smoking cessation interventions targeting Chinese Canadian immigrant smokers as well as smokers in China.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".