Assessing beliefs and risk perceptions on smoking and smoking cessation in immigrant Chinese adult smokers residing in Vancouver, Canada: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVES: We aimed to conduct culturally-based participatory research to assess cultural and belief contexts for smoking behaviours within Mandarin and Cantonese communities. Outcome variables were smoking-related knowledge, smoking patterns, attitudes and beliefs, and perceived barriers and facilitators to successful cessation. DESIGN: A community-based approach was applied involving smokers, community key-informants and professionals in study design and implementation. Initially, focus groups were conducted and findings were used to develop study instrument. Participants responded once to study questionnaire after informed consent. SETTING: Community based in the Greater Vancouver Area, Canada. PARTICIPANTS: 16 Chinese smokers participated in focus groups and subsequently, 167 current Chinese immigrant (137 males and 30 females) smokers from Mandarin and Cantonese communities, recruited with the help of community agencies and collaborating physicians, were enrolled in a cross-sectional study. RESULTS: We found that a majority believed smoking was harmful on their health. Younger smokers (<35 years of age) did not mind smoking in front of young children compared to older smokers (≥35 years of age) (p<0.001). People with high school or lower levels of education believed that they would benefit more from smoking than suffering from withdrawal symptoms compared to better educated smokers (p<0.05). Mandarin smokers were significantly more likely to encourage others to quit than Cantonese smokers (p<0.05). Many indicated not receiving adequate support from care providers and lack of access to culturally and linguistically appropriate cessation programmes impacted on their ability to quit smoking. CONCLUSIONS: Our study highlighted the importance of tobacco beliefs and perceptions among Mandarin and Cantonese speaking immigrants with limited access to healthcare information and for younger smokers whose attention to health consequences of smoking may be limited as well. Study participants were generally aware of the health risks and were willing to quit. Access to appropriate cessation programmes would fulfil their willingness.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".