Lessons Learned Implementing a Province-Wide Smoking Cessation Initiative in Ontario’s Cancer Centres
Bibliographic record
Abstract
PURPOSE: A large body of evidence clearly shows that cancer patients experience significant health benefits with smoking cessation. Cancer Care Ontario, the provincial agency responsible for the quality of cancer services in Ontario, has undertaken a province-wide smoking cessation initiative. The strategies used, the results achieved, and the lessons learned are the subject of the present article. METHODS: Evidence related to the health benefits of smoking cessation in cancer patients was reviewed. A steering committee developed a vision statement for the initiative, created a framework for implementation, and made recommendations for the key elements of the initiative and for smoking cessation best practices. RESULTS: New ambulatory cancer patients are being screened for their smoking status in each of Ontario's 14 regional cancer centres. Current or recent smokers are advised of the benefits of cessation and are directed to smoking cessation resources as appropriate. Performance metrics are captured and used to drive improvement through quarterly performance reviews and provincial rankings of the regional cancer centres. CONCLUSIONS: Regional smoking cessation champions, commitment from Cancer Care Ontario senior leadership, a provincial secretariat, and guidance from smoking cessation experts have been important enablers of early success. Data capture has been difficult because of the variety of information systems in use and non-standardized administrative and clinical processes. Numerous challenges remain, including increasing physician engagement; obtaining funding for key program elements, including in-house resources to support smoking cessation; and overcoming financial barriers to access nicotine replacement therapy. Future efforts will focus on standardizing processes to the extent possible, while tailoring the approaches to the populations served and the resources available within the individual regional cancer programs.
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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 | 0.061 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".