Smoking in Substance Use Disorder Patients: Prevalence, Comorbidities, Impulsivity, and Patterns of Readiness to Change
Bibliographic record
Abstract
INTRODUCTION: Tobacco use is highly prevalent in individuals with other substance use disorders (SUDs) and is associated with greater smoking-related illnesses and premature death. To inform intervention strategies, the current study examined the prevalence and clinical features of smoking, including motivation for change, comorbid psychiatric symptoms, and self-regulatory indicators, in a large sample of treatment-seeking SUD patients. AIMS AND METHODS: Participants were 1893 patients in three clinical programs who were assessed for tobacco use, other substance misuse, psychiatric symptoms, measures of self-regulation (ie, delay discounting, UPPS-P impulsive behavior scales, mindfulness), and readiness rulers (ie, readiness, importance, and confidence). Psychiatric and impulsivity indicators were further examined among patients in precontemplative, contemplative, and actively ready stages of change. RESULTS: Overall, 73.7% of patients reported combustible tobacco use, with almost half reporting that they were contemplating or actively ready to change. Patients who smoked reported significantly greater psychiatric symptoms, higher illicit substance use, more impulsive delay discounting, and personality traits (ie, positive and negative urgency, lack of premeditation, and sensation seeking), and lower mindfulness. Those who smoked and were actively ready to change their behavior were older, smoked fewer cigarettes per day, and exhibited significantly less impulsive delay discounting and lack of perseverance. CONCLUSIONS: The prevalence of smoking is high in SUD treatment programs and is associated with greater psychiatric symptom severity, substance misuse, psychiatric severity, and impulsivity. Differing levels of readiness suggest three distinct intervention pathways.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".