Integration of an evidence-based tobacco cessation program into a substance use disorders program to enhance equity of treatment access for northern, rural, and remote communities
Bibliographic record
Abstract
Integrating tobacco cessation interventions into substance use disorder (SUD) programs is recommended, yet few are implemented into practice. This translational research implementation study was designed to integrate an evidence-based tobacco cessation intervention into a 2-week hospital outpatient SUD program that served a rural municipality and 33 remote Indigenous communities. Objectives included determining tobacco use prevalence, intervention uptake, and staffing resources required for intervention delivery. A series of 1-hr tobacco and health/well-being interactive education and behavior-change groups were developed for the SUD program to create a central access point to offer an evidence-based, intensive tobacco cessation intervention that included an initial counseling/planning session and nine post-SUD treatment follow-ups (weekly month 1; biweekly month 2; and 3, 6, and 12 months). Group sign-in data included age, gender, community, tobacco use, and interest in receiving tobacco cessation help. Thirty-two groups (April 2018 to February 2019) were attended by 105 people from 22 communities-56% were female, mean age = 30.9 (±7.3; 93% <45 years), 86% smoked, and 38% enrolled in the intensive tobacco cessation intervention. The age-standardized tobacco use ratio was two times higher than would be expected in the general rural population in the region. Average staff time to provide the intervention was 1.5-2.5 hr/week. Results showed that a Healthy Living group integrated into SUD programming provided a forum for tobacco education, behavior-change skills development, and access to an intensive tobacco cessation intervention for which enrollment was high yet the intervention could be delivered with only a few staff hours a week.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".