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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".