Implementing smoking cessation within cancer treatment centres and potential economic impacts
Bibliographic record
Abstract
BACKGROUND: Although the health benefits of smoking cessation in newly diagnosed cancer patients are well established, systematic efforts to help cancer patients stop smoking have rarely been implemented in cancer centres. METHODS: Starting in 2012, the 14 regional cancer centres overseen by Cancer Care Ontario in the province of Ontario, Canada began to screen ambulatory cancer patients for their smoking status, to provide smokers with advice on the health benefits of quitting and to offer referral to smoking cessation services. Multiple initiatives were undertaken to educate healthcare providers and patients on the health benefits of cessation. Critical to the success of the initiative was strong leadership from Cancer Care Ontario executives and regional vice presidents, advice from an advisory committee of smoking cessation experts, engagement of regional champions and support from a provincial secretariat. The quarterly review of performance metrics was an important driver of change. RESULTS: Most cancer centres now screen in excess of 75% of ambulatory patients but rates for the acceptance of a referral to smoking cessation services remain low (less than 25%). Introduction of an opt-out referral process appears to increase referral acceptance. Economic analyses suggest that smoking cessation is cost-effective in a cancer centre environment. CONCLUSIONS: Although there are barriers to the implementation of smoking cessation in cancer centres, it is possible to change the culture to one in which smoking cessation is considered part of high-quality treatment.
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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 | 0.025 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".