Chinese immigrant men smokers’ sources of cigarettes in Canada: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Immigrants often experience economic hardship in their host country and tend to belong to economically disadvantaged groups. Individuals of lower socioeconomic status tend to be more sensitive to cigarette price changes. This study explores the cigarette purchasing patterns among Chinese Canadian male immigrants. METHODS: Semi-structured in-depth interviews were conducted with 22 Chinese Canadian immigrants who were smoking or had quit smoking in the last five years. RESULTS: Because of financial pressures experienced by participants, the high price of Canadian cigarettes posed a significant challenge to their continued smoking. While some immigrants bought fully-taxed cigarettes from licensed retailers, more often they sought low-cost cigarettes from a variety of sources. The two most important sources were cigarettes imported during travels to China and online purchases of Chinese cigarettes. The cigarettes obtained through online transactions were imported by smoking or non-smoking Chinese immigrants and visitors, suggesting the Chinese community were involved or complicit in sustaining this form of purchasing behavior. Other less common sources included Canada-USA cross border purchasing, roll your-own pouch tobacco, and buying cigarettes available on First Nations reserves. CONCLUSIONS: Chinese Canadian immigrant men used various means to obtain cheap cigarettes. Future research studies could explore more detailed features of access to expose gaps in policy and improve tobacco regulatory frameworks.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".