Do demographic and socioeconomic characteristics underpin differences in youth smoking initiation across Canadian provinces? Evidence from the Canadian Community Health Survey (2015–2018)
Bibliographic record
Abstract
INTRODUCTION: Youth initiation may drive differences in smoking prevalence across Canadian provinces. Provincial differences in initiation relate to tobacco control strategies and public health funding, but have also been attributed to population characteristics. We test this hypothesis by examining the extent to which seven characteristics-immigration, language, family structure, education, income, home ownership and at-school status-explain differences in initiation across provinces. METHODS: We used data from 16 897 youth aged 12 to 17 years in the Canadian Community Health Survey collected from 2015 to 2018. To examine the proportion of provincial differences explained by population characteristics, we compared average marginal effects (AMEs) from partially and fully adjusted models regressing "having ever initiated" on province and other characteristics. We also tested interactions to examine differences in the association between population characteristics and initiation across provinces. RESULTS: Initiation varied from 4% in British Columbia to 10% in Quebec. Being born in Canada, speaking French, not living in a two-parent household, being in the lowest household income quintile, having parents without postsecondary education, living in rented accommodation and not being in school were each associated with initiation. Taking these results into consideration, the AME of residing in another province compared with Quebec was attenuated by between 3% and 9%. Family structure and household income were more strongly associated with initiation in the Atlantic region and Manitoba, but not in Quebec. CONCLUSION: Differences in initiation between Quebec and other provinces are unlikely to be substantially explained by their demographic or socioeconomic composition. Reprioritizing tobacco control and public health funding are likely key in attaining the "tobacco endgame" across provinces.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".