Ethno-specific preferences of cigarette smoking and smoking initiation among canadian immigrants – A multi-level analysis
Bibliographic record
Abstract
Background: Cigarette smoking is the leading cause of preventable morbidity and mortality worldwide. Over the last decade, increased immigration has significantly shifted Canada’s demographic profile. According to a 2011 National Household Survey, approximately 20.6% of the Canadian population was immigrants, the highest among the G8 countries. It is estimated that by 2031, one-in-three Canadian’s will be an immigrant. This study examined the ethno-specific preference of cigarette smoking and smoking initiation among Canadian immigrants. Methods: This study used data from the 2013 to 2014 combined cycles of the Canadian Community Health Survey. This was a nationally generalizable, telephone-based survey that included a total of 130,000 respondents, aged 12 years or older. Ethnic differences in the preference of cigarette use among Canadian immigrant groups were determined. A three-level mixed effects logistic regression model was used to estimate the effect of ethnicity on the likelihood of smoking initiation after migration to Canada. Results: In our study, 82% of respondents were native-born Canadians (one group), while the rest were immigrants (six groups=18%). Results of the logistic regression analysis revealed statistically significant differences in the number of cigarettes smoked daily ( P =0.0001), age of smoking onset ( P =0.0001), and smoking initiation ( P =0.0001) between Canadian-born and immigrant participants. Immigrant smokers in Canada were significantly more likely to be younger, single, Caucasian, females with high income and post-secondary education ( P =0.0001). Conclusion: The results of our study suggest that Caucasian female immigrants in Canada initiated smoking at a younger age and smoked more cigarettes than any other immigrant group or native-born Canadians. This is a particularly interesting finding as Caucasian female immigrants may not be considered a vulnerable or at-risk population. To be effective, tobacco strategies specifically tailored for this overlooked population would require increased awareness, culturally appropriate initiatives, and gender-specific interventions. Keywords: cigarette smoking, initiation, ethnicity, immigrants, Canada
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".