Provision of recent advice to quit smoking by healthcare providers in Canada: Findings from the 2022 Canada International Tobacco Control Smoking and Vaping Survey
Bibliographic record
Abstract
Objectives: Among Canadian adults who currently or formerly smoked cigarettes, we examined: consultations with healthcare providers (HPs) in the last two years; among those, the proportion who received any smoking cessation advice and the type of support recommended; and whether receiving cessation advice varied by social determinants of health (SDH) and health conditions. Methods: Data are from the 2022 Canada International Tobacco Control Survey (online; August-December 2022). Eligible respondents included 1163 adults who smoked daily for at least two years or quit smoking in the last two years, but previously smoked daily. SDH and health variables included: sociodemographics, financial insecurity, depression/anxiety, lung disease, and alcohol use. Results: Almost half consulted an HP (48.7 %); among those, 51.0% received cessation advice: 46.8 % were advised to use nicotine replacement therapy, 32.0 % varenicline, 14.0 % bupropion, 13.3 % a quitline, 14.5 % a smoking cessation program, and 2.6 % an e-cigarette. Adults with heart disease were more likely to receive advice to quit [odds ratio (OR) 2.74, 95 % confidence interval (CI):1.26,5.98)]. Among adults who were smoking, those who had no interest in quitting (OR 0.25, 95 % CI: 0.09,0.68) or reported that they wanted to quit a little/somewhat (OR 0.42, 95 % CI: 0.22,0.80) were less likely to receive advice compared to those who wanted to quit smoking a lot. There were no significant differences by SDH, mental health, lung disease, or alcohol use. Conclusion: The low rate of smoking cessation advice from HPs underscores the need for systematic engagement across clinical settings to encourage quitting smoking and offer evidence-based methods to support cessation.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".