Physical multimorbidity and quit outcomes in a publicly funded smoking cessation programme
Bibliographic record
Abstract
OBJECTIVES: To examine the association between physical multimorbidity and 6-month quit outcomes among treatment-seeking smokers. METHODS: We analysed data from 120 732 adults enrolled in Ontario's largest publicly funded smoking cessation programme. At enrolment, participants self-reported zero, one or two or more chronic physical health conditions. The primary outcome was 7-day point prevalence abstinence at 6 months. We used mixed-effects logistic regression to assess the association between multimorbidity and quit outcomes, adjusting for demographic and tobacco use characteristics. RESULTS: Of participants, 39.4% reported no conditions, 26.8% reported one and 33.8% reported two or more. Those with multimorbidity were older, had lower socioeconomic status and showed higher tobacco dependence but also greater motivation to quit. Compared to individuals without comorbidities, those with one condition (OR = 0.94, 95% CI: 0.90-0.98) and those with two or more (OR = 0.81, 95% CI: 0.77-0.85) had lower odds of quitting. Mental health conditions further reduced quit success among those with physical multimorbidity. DISCUSSION: Physical multimorbidity is associated with 19% lower odds of cessation success despite high motivation to quit. Tailored, intensive cessation support may improve outcomes for this high-risk group.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".