Treatment Outcomes of a Tailored Smoking Cessation Programme for Individuals Accessing Addiction Treatment Services
Bibliographic record
Abstract
Background: Individuals with substance use disorders (SUD) have disproportionately higher smoking prevalence and tobacco-related mortality than the general population. This high prevalence of smoking warrants a need for targeted tobacco treatment efforts.\nObjectives: To examine smoking cessation outcomes and predictors of successful smoking cessation among individuals with SUD accessing a tobacco dependence clinic (TDC) within Addiction Services.\nMethods: Based on clinical guidelines, participants of the TDC received behavioural therapy combined with tailored pharmacotherapy for tobacco treatment (at no cost). A retrospective chart review from 678 participants enrolled in the TDC between Sept 2007 and Dec 2011 was analyzed. 7-day point-prevalence abstinence (validated by expired carbon monoxide) at end-of-treatment was the main outcome measure.\nResults: For individuals who completed the program (n=523), the abstinence rate was 40.3%. Significant predictors of successful smoking abstinence at the end-of-treatment were: a) having a lower expired CO level at baseline, and b) staying in treatment for a greater number of weeks.\nConclusions: Tobacco treatment tailored to the needs of individuals with SUD is an important approach to reduce the disproportionate tobacco-related morbidity and mortality in this population. Specialized tobacco treatment in addiction service settings is well received by clients who are motivated to quit smoking.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".