Smoking Cessation Training and Treatment: Options for Cancer Centres
Bibliographic record
Abstract
Patients who achieve smoking cessation following a cancer diagnosis can experience an improvement in treatment response and lower morbidity and mortality compared to individuals who continue to smoke. It is therefore imperative for publicly funded cancer centres to provide appropriate training and education for healthcare providers (HCP) and treatment options to support smoking cessation for their patients. However, system-, practitioner-, and patient-level barriers exist that hamper the integration of evidence-based cessation programs within publicly funded cancer centres. The integration of evidence-based smoking cessation counselling and pharmacotherapy into cancer care facilities could have a significant effect on smoking cessation and cancer treatment outcomes. The purpose of this paper is to describe the elements of a learning health system for smoking cessation, implemented and scaled up in community settings that can be adapted for ambulatory cancer clinics. The core elements include appropriate workflows enabled by technology, thereby improving both practitioner and patient experience and effectively removing practitioner-level barriers to program implementation. Integrating the smoking cessation elements of this program from primary care to cancer centres could improve smoking cessation outcomes in patients attending cancer clinics.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.012 |
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".