The geographic distribution and community correlates of electronic cigarette use in Canada
Bibliographic record
Abstract
OBJECTIVES: Electronic cigarettes and other novel electronic nicotine delivery systems (ENDS) have grown rapidly in popularity and accessibility. In this study, we compiled a large sub-provincial dataset on smoking and vaping behaviour in Canada to inform targeted surveillance and prevention. METHODS: Twelve national-level survey datasets were concatenated. Multilevel models were used to derive precision-weighted estimates of census division-level smoking and ENDS use prevalence, adjusted for age, sex/gender, and data source. We developed visualizations of the geography of smoking and ENDS use across Canada and used Census Divisions for spatially explicit correlational analyses of community characteristics associated with vaping. RESULTS: The age- and sex-adjusted prevalence of past-month (i.e., current) ENDS use in Canada was 4%, with higher estimates observed in several Atlantic provinces: New Brunswick (5.6%), Prince Edward Island (4.8%), Nova Scotia (4.7%), and Newfoundland and Labrador (4.5%) followed by Manitoba (4.1%). Estimates for the remaining provinces were below 4%. The prevalence of ENDS use varied considerably across CDs, even in provinces where vaping was generally uncommon. Suburban and exurban communities in Ontario and Quebec demonstrated especially high ENDS use. Spatial analyses revealed select correlations with community factors such as economic composition. CONCLUSION: Sub-provincial data revealed geographical variability in ENDS use across Canada. Localized surveillance and prevention efforts may be improved by considering the community features associated with high rates of use, and benchmarking regional regulations on the advertising and sales of ENDS products to communities with lower estimated rates of use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".