Evolution of a Systematic Approach to Smoking Cessation in Ontario’s Regional Cancer Centres
Bibliographic record
Abstract
Smoking cessation after a cancer diagnosis can significantly improve a person's prognosis, treatment efficacy and safety, and quality of life. In 2012, Cancer Care Ontario (now part of Ontario Health) introduced a Framework for Smoking Cessation, to be implemented for new ambulatory cancer patients at the province's 14 Regional Cancer Centres (RCCs). Over time, the program has evolved to become more efficient, use data for robust performance management, and broaden its focus to include new patient populations and additional data collection. In 2017, the framework was revised from a 5As to a 3As brief intervention model, along with an opt-out approach to referrals. The revised model was based on emerging evidence, feedback from stakeholders, and an interim program evaluation. Results showed an initial increase in referrals to cessation services. Two indicators (tobacco use screening and acceptance of a referral) are routinely monitored as part of Ontario Health's system-wide performance management approach, which has been identified as a key driver of change among RCCs. Due to the COVID-19 pandemic, many RCCs reported a decrease in these indicators. RCCs that were able to maintain a high level of smoking cessation activities during the pandemic offer valuable lessons, including the opportunity to swiftly leverage virtual care. Future directions for the program include capturing data on cessation outcomes and expanding the intervention to new populations. A focus on system recovery from COVID-19 will be paramount. Smoking cessation must remain a core element of high-quality cancer care, so that patients achieve the best possible health benefits from their treatments.
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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 | 0.109 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".