Prospective, Cluster-Randomized Trial to Implement the Ottawa Model for Smoking Cessation in Diabetes Education Programs in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE To test whether a practice-level intervention to promote the systematic identification, treatment, and follow-up of smokers (the Ottawa Model for Smoking Cessation [OMSC]) would improve long-term abstinence rates among smoker-patients with type 2 diabetes or prediabetes receiving care from diabetes education programs in Ontario, Canada. RESEARCH DESIGN AND METHODS The Tobacco Intervention in Diabetes Education study was a matched-pair, cluster-randomized clinical trial. Within each pair, sites were randomly allocated to either an OMSC intervention (n = 7) or a wait-list control (WLC) condition (n = 7). Diabetes education programs in the OMSC group introduced standardized processes to identify smokers and routinely provided smoking cessation interventions and follow-up. Smokers in the OMSC group received counseling, a discount card to partially cover the cost of smoking cessation medication, and follow-up telephone calls over a 6-month period. Diabetes education programs in the WLC condition were offered the OMSC intervention after a 1-year waiting period. Smokers in the WLC group received usual care for smoking cessation from their diabetes educator. The primary end point was carbon monoxide (CO)–confirmed 7-day point prevalence abstinence from smoking at 6-month follow-up. RESULTS A total of 313 smokers (OMSC group n = 199, WLC group n = 114) with diabetes or prediabetes were enrolled. The CO-confirmed abstinence rate at 6 months was 11.1% in the OMSC group versus 2.6% in the WLC group (odds ratio 3.73 [95% CI 1.20, 11.58]; P = 0.02). CONCLUSIONS Implementation of the OMSC in diabetes education programs resulted in clinically and statistically significant improvements in long-term abstinence among smokers with diabetes or prediabetes.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".