Smoking Cessation Among Persons With Co-Occurring Substance Use Disorder and Mental Illness
Bibliographic record
Abstract
Abstract Aims: A history of either a substance use disorder (SUD) or psychiatric disorder (PD) is associated with tobacco use. However, there is limited information available on tobacco dependence treatment outcomes among individuals with co-occurring SUD and PD. Methods: Data from 202 participants enrolled in a tobacco dependence treatment program in an outpatient clinic setting were analysed. Findings: In multivariate analysis, having a history of SUD only (OR =.11, 95% CI = .02–.76) and having a co-occurring SUD and PD (OR = .13, 95% CI = .02–.81), as compared to having neither, were significant predictors of a lower likelihood of achieving smoking abstinence. Conclusions: A history of SUD and PD is an important predictor of poor smoking cessation outcomes; however, using more intensive, tailored approaches to tobacco dependence treatment appears to be promising. Future studies may need to further address the nature of tobacco dependence treatment in settings were PDs and other SUDs are managed in order to achieve optimal outcomes.
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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 | 0.001 | 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 | 0.001 |
| 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".