Increasing Rates of Tobacco Treatment Delivery in Primary Care Practice: Evaluation of the Ottawa Model for Smoking Cessation
Bibliographic record
Abstract
PURPOSE: We report on the effectiveness of the Ottawa Model for Smoking Cessation (OMSC), a multicomponent knowledge translation intervention, in increasing the rate at which primary care providers delivered smoking cessation interventions using the 3 A's model-Ask, Advise, and Act, and examine clinic-, provider-and patient-level determinants of 3 A's delivery. METHODS: We examined the effect of the knowledge translation intervention in 32 primary care practices in Ontario, Canada, by assessing a cross-sectional sample of patients before the implementation of the OMSC and a second cross-sectional sample following implementation. We used 3-level modeling (clinic, clinician, patient) to examine the main effects and predictors of 3 A's delivery. RESULTS: Four hundred eighty-one primary care clinicians and more than 3,500 tobacco users contributed data to the evaluation. Rates of delivery of the 3 A's increased significantly following program implementation (Ask: 55.3% vs 71.3%, P <.001; Advise: 45.5% vs 63.6%, P <.001; Act: 35.4% vs 54.4%, P <.001). The adjusted odds ratios (AOR) for the delivery of 3 A's between the pre- and post-assessments were AOR = 1.94; (95% CI, 1.61-2.34) for Ask, AOR = 1.92; (95% CI, 1.60-2.29) for Advise, and AOR = 2.03; (95% CI, 1.71-2.42) for Act. The quality of program implementation and the reason for clinic visit were associated with increased rates of 3 A's delivery. CONCLUSIONS: Implementation of the OMSC was associated with increased rates of smoking cessation treatment delivery. High quality implementation of the OMSC program was associated with increased rates of 3 A's delivery.
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 | 0.044 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".