Predictors of smoking cessation in adults from two low socio-economic status communities in Montreal, Canada
Bibliographic record
Abstract
Objectives. Few studies have identified longitudinal predictors of smoking cessation in disadvantaged communities. This study identified predictors of cessation in a 5-year longitudinal cohort of adults aged 18-65 years and living in low-income, inner-city neighborhoods of Montreal, Canada. Methods. Secondary analysis of data from the non-randomized evaluation of Coeur en Sante St. Henri, a community-based intervention program designed to decrease cardiovascular disease risk (CVD) factors. Data on lifestyle behaviors were collected in telephone interviews of a representative sample of residents at baseline and five years later. Independent predictors of cessation were identified among 303 subjects who smoked at baseline, using multiple logistic regression. Results. After 5 years, 20% of baseline smokers reported quitting including 22% of female smokers, and 17% of male smokers. From among 7 potential predictors only two were retained in multivariable analysis, including having a post-secondary or higher education relative to secondary school or less (OR=1.88, 95%CI: 1.01-3.51), and number of cigarettes smoked per day (OR=0.95, 95%CI: 0.91-0.98). Conclusions. Few predictors of cessation emerged in this disadvantaged community. It is notable that even in a disadvantaged community, increased education predicts cessation. Improved understanding of the mechanisms by which education leads to higher quit rates may help the development of cessation programs targeting disadvantaged communities.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".