Bibliographic record
Abstract
Background: Evidence indicates that smoking cessation improves the effectiveness of treatment and likelihood of survival among all cancer patients, not just those with tobacco-related disease, yet smoking is rarely addressed in oncology practice. Prior to 2016, only 3 provinces in Canada (out of a total of 10 provinces and three territories) reported implementation of smoking cessation for ambulatory cancer patients. Aim: Based on this evidence, the Canadian Partnership Against Cancer (CPAC) implemented a systems change initiative to promote adoption of evidence-based smoking cessation within provincial and territorial cancer systems across Canada. Methods: In 2016, CPAC funded seven provinces and two territories over a 15-month period to plan, implement or evaluate integration of evidence-based smoking cessation for ambulatory cancer patients within cancer systems. Funds were used to plan (2 provinces and 2 territories), implement (3 provinces) or evaluate (2 provinces) systematic, evidence-based approaches to smoking cessation within ambulatory cancer care settings (e.g., establishing routine systems for identification of smoking cancer patients and system to support patients to quit). Funds could not be used for direct service delivery (e.g., cessation counseling). Results: After 15-months of funding from CPAC, 6 provinces reported implementation of smoking cessation for ambulatory cancer patients. The remaining province and 2 territories funded by CPAC reported development of plans for adoption of smoking cessation for cancer patients in the future. Within provinces reporting implementation of smoking cessation for cancer patients, between 65%-97% of ambulatory cancer patients were screened for smoking status; 22%-80% of these patients were offered a referral to cessation services, and 21%-45% of cancer patients accepted a referral. Conclusion: Despite provincial and territorial variations in readiness to uptake evidence-based smoking cessation for cancer patients, CPAC's approach has led to substantial progress in adoption of this approach across Canada. While progress has been made, adoption of smoking cessation and relapse prevention by cancer systems is not yet widespread in Canada. Scale-up to remaining provinces and territory, and spread within existing provinces and territories is required to reach all cancer patients and families who require support to quit smoking. Framing smoking cessation as a therapeutic intervention, not prevention, and a routine part of cancer treatment will be critical for sustainability of this work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".