Mind the Gap: Disparities in Cigarette Smoking in Canada
Bibliographic record
Abstract
OBJECTIVES: The Government of Canada has proposed an 'endgame' target for cigarette smoking that aims to reduce prevalence below 5% by 2035. To meet this difficult goal, it will be necessary to identify populations where interventions will (1) have the greatest impact in reducing the number of smokers and (2) have the greatest impact in addressing smoking disparities. METHODS: Using data from the Canadian Community Health Survey, smoking prevalence was estimated for populations that differed with respect to demographic, substance use, and mental health factors. Risk difference, relative risk, and attributable disparity number, which describes the magnitude of the potential impact if the disparity were addressed, were calculated for each group. RESULTS: The strongest disparities (relative risk ⩾ 2) were associated with immigration status (for women), substance use, marital status, and lifetime experience of mental health or substance use disorders. The smallest disparities (relative risk ⩽ 1.5) were associated with sexual orientation, household income, immigration status (men), and province of residence. The groups with the largest attributable disparity number were among those who used cannabis, and those who were not immigrants, not married, and white. CONCLUSIONS: Disparities which were both strong and had a large potential impact on prevalence overall were found for populations facing mental health and substance use concerns. Differences in rankings were found depending on the scale of the measure. Addressing disparities in smoking rates is an important component of developing tobacco endgame strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".