Evaluating the Associations Between Exposure to Tobacco Interventions During Inpatient Treatment and Substance Use Outcomes: Findings From a Natural Experiment
Bibliographic record
Abstract
OBJECTIVE: In this study, we took advantage of a natural experiment that occurred within a substance use disorders (SUD) treatment setting which first saw the implementation of an evidence-based practice (EBP) for tobacco cessation, followed by the implementation of a tobacco-free policy (TFP) that included a campus-wide tobacco ban. We sought to examine how implementation of the EBP and TFP was associated with substances use outcomes, in addition to tobacco use, up to 3-months posttreatment. METHODS: Data were collected from patients in a substance use disorders treatment program at baseline, discharge, 1-, and 3-months posttreatment. Using a quasi-experimental design and generalized estimating equations, we modelled how patients' (N = 480) exposure to one of 3 interventions (1: treatment as usual [TAU], 2: EBP, and 3: EBP + TFP) was associated with overall abstinence from tobacco, alcohol, and other substances over time. Measures of tobacco use frequency, amount, and quit attempts were also modelled among a sub-sample of participants who self-reported using tobacco before treatment. RESULT: Exposure to the EBP + TFP was associated with increased tobacco abstinence (odds ratio [OR] = 1.93, 95% confidence interval [CI] [1.29, 2.90]) over time, including decreases in tobacco use frequency (OR = 0.78, 95% CI [0.68, 0.89]) and amount (OR = 0.80, 95% CI [0.67, 0.96]), and increased in likelihood of making a quit attempt (OR = 1.75, 95% CI [1.10, 2.80]) compared to TAU. Exposure was not associated with alcohol and/or other substance use. CONCLUSIONS: Comprehensive tobacco interventions that include EBP + TFP can promote tobacco cessation and reduced tobacco use following inpatient SUD treatment, without adversely affecting the use of other substances.
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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 | 0.029 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".