The roll-your-own cigarette market in Canada: a cross-sectional exploratory study
Bibliographic record
Abstract
BACKGROUND: Even though the use and prevalence of roll-your-own cigarettes (RYO) has been declining over the past decades, RYO remains important. Given the paucity of research examining RYO use, there is a need to better understand the current and potential future context of RYO use. METHODS: Data from the 2002 Canadian Tobacco Use Monitoring Survey (CTUMS) were used to examine RYO tobacco use among 23,341 Canadians aged 15 and older. Logistic regression models were conducted to examine factors which differentiate smokers who smoke RYO tobacco all of the time, most of the time or sometimes from smokers who do not smoke RYO tobacco. RESULTS: We found that 17% (n = 925,000) of current smokers in Canada reported smoking RYO. When compared to manufactured cigarette (MC) smokers, RYO users were heavier smokers, more addicted to nicotine, and less likely to consider quitting smoking. Lower income smokers were more likely to smoke RYO tobacco compared to smokers with high income. Conversely, smokers who had completed secondary school or university were less likely to smoke RYO tobacco compared to smokers who had not completed secondary school. CONCLUSION: This study demonstrates that RYO tobacco use is not a negligible problem within Canada and provides valuable new insight for developing future tobacco control initiatives for this population of smokers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".