Smoking cessation for hospitalized smokers: An evaluation of the “Ottawa Model”
Bibliographic record
Abstract
INTRODUCTION: Interventions for hospitalized smokers can increase long-term smoking cessation rates. The Ottawa Model for Smoking Cessation (the "Ottawa Model") is an application of the "5 A's" approach to cessation, customized to the hospital setting. This study evaluated the impact of implementing the Ottawa Model in 9 hospitals in eastern Ontario. METHODS: The RE-AIM (Reach, Efficacy, Adoption, Implementation, and Maintenance) framework was used to evaluate the intervention. Trained outreach facilitators assisted 9 hospitals to implement the Ottawa Model; program delivery was then monitored over a 1-year period using administrative data and data from a follow-up database. A before-and-after study was conducted to gauge the effect of the Ottawa Model program on cessation rates 6 months after hospitalization. Self-reports of smoking cessation were biochemically confirmed in a random sample of patients, and all cessation rates were corrected for potential misreporting. RESULTS: Sixty-nine percent of the expected number of smokers received the Ottawa Model intervention. Controlling for hospital, the confirmed 6-month continuous abstinence rate was higher after, than before, introduction of the Ottawa Model (29.4% vs. 18.3%; odds ratio = 1.71, 95% CI = 1.11-2.64; Z = 2.43; I(2) = 0%; p = .02). The intervention was more likely to accomplish counseling for smokers than delivery of medications or postdischarge follow-up. Attitudinal, managerial, and environmental challenges to program implementation were identified. DISCUSSION: Trained outreach facilitators successfully implemented the Ottawa Model in 9 hospitals leading to significantly higher long-term cessation rates. The public health implications of systematic cessation programs for hospitalized smokers are profound.
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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 | 0.034 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".