Access to smoking cessation resources for cancer patients across Canada and systemic barriers to implementing change in systems process.
Bibliographic record
Abstract
e18539 Background: Though smoking by cancer patients and survivors causes adverse cancer treatment outcomes, most cancer patients do not receive evidence-based treatment for smoking cessation. The purpose of this study was to evaluate practice patterns and barriers across all provinces and territories within Canada. Methods: Beginning in 2017, the Canadian Partnership Against Cancer (CPAC) convened initiatives to assess smoking cessation resources for cancer patients across all provinces and territories in Canada using the Pan-Canadian Tobacco Cessation and Cancer Care Network (Network). Key strategic meetings were held to assess access and barriers by Network members including representatives from governmental departments responsible for tobacco control, public health, indigenous health authorities, patient and family advisors, and cancer care executives from each province or territory. Results: Existing smoking cessation resources were available to cancer patients in 56% (70/125) of cancer care settings across Canada. Most jurisdictions used an ask-advise-refer (AAR) model with approximately half of jurisdictions using telephone, text, or online counseling, and a minority (5) using internal or external smoking cessation services. Few jurisdictions offered free pharmacotherapy (5) or support to family members (4). The dominant reported barrier to adoption was a need for culture change (77% or provinces and territories) followed by access to free or low-cost pharmacotherapy (52%). Competing priorities in cancer care (28%), limited use of electronic information systems (28%), and lack of quality indicators/accreditation standards (15%) were perceived as barriers less frequently. Development of a pan-Canadian vision and framework including cost considerations were reported as opportunities to support accelerated uptake of smoking cessation with cancer systems. Conclusions: Addressing barriers and providing a pan-Canadian framework are opportunities to promote widespread implementation of evidence-based smoking cessation for cancer patients. Continued Network collaboration will facilitate real-time assessment of practice change and adoption.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".