Public health impact of a novel smoking cessation outreach program in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Provision of evidence-based smoking cessation treatment may contribute to health disparities if barriers to treatment are greater for more disadvantaged groups. We describe and evaluate the public health impact of a novel outreach program to improve access to smoking cessation treatment in Ontario, Canada. METHODS: We partnered with Public Health Units (PHUs) located across the province to deliver single-session workshops providing standardized evidence-based content and 10 weeks (2007-2008) or 5 weeks (2008-2016) of nicotine replacement therapy (NRT). Participants completed a baseline assessment and were followed up by phone or e-mail at 6 months. We used the RE-AIM (Reach, Effectiveness, Adoption, Implementation and Maintenance) framework to evaluate the public health impact of the program from 2007 to 2016. Given the iterative design and changes in implementation over time, data is presented annually or bi-annually. RESULTS: There were 26,122 enrollments from 2007 to 2016. Between 31 and 442 workshops were held annually. The annual reach was estimated to be 0.1-0.3% of eligible smokers in Ontario. Participants were older, smoked more heavily, had a lower household income, were more likely to be female and be diagnosed with a mood or anxiety disorder, and less likely to have a postsecondary degree compared to average Ontario smokers eligible for participation. The intervention was effective; at 6-month follow-up 22-33% of respondents reported abstinence from smoking. Adoption by PHUs was 81% by the second year of operation and remained high (72-97%) thereafter, with the exception of 2009-2010 (33-56%) when the program was temporarily unavailable to PHUs due to lack of funding. Implementation at the organizational level was not tracked; however, at the individual level, approximately half of participants used most or all of the NRT received. On average, maintenance of the program was high, with PHUs conducting workshops for 7 of the 10 years (2007-2016) and 4 of the 5 most recent years (2012-2016). CONCLUSIONS: The smoking cessation program had a high rate of adoption and maintenance, reached smokers over a large geographic area, including individuals more likely to experience disparities, and helped them make successful quit attempts. This novel model can be adopted in other jurisdictions with limited resources.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".