Implementation of a Comprehensive Smoking Cessation Program in Cancer Care
Bibliographic record
Abstract
Background: Quitting smoking after a cancer diagnosis maximizes treatment-related effects, improves prognosis, and enhances quality of life. However, smoking cessation (sc) services are not routinely integrated into cancer care. The Princess Margaret Cancer Centre implemented a digitally-based sc program in oncology, leveraging an e-referral system (cease) to screen all new ambulatory patients, provide tailored education and advice on quitting, and facilitate referrals. Methods: We adopted the Framework for Managing eHealth Change to guide implementation of the sc program by integrating 6 key elements: governance and leadership, stakeholder engagement, communication, workflow analysis and integration, monitoring and evaluation, and training and education. Results: Incorporating elements of the Framework, we used extensive stakeholder engagement and strategic partnerships to establish a sc program with organizational and provincial accountability. Existing electronic patient-reported assessments were changed to integrate cease. Clinic audits and staff engagement allowed for analysis of workflow, ongoing monitoring and evaluation that aided in establishing a communication strategy, and development of cancer-specific education for patients and health care providers. From April 2016 to March 2018, 22,137 new patients were eligible for screening. Among those new patients, 13,617 (62%) were screened, with 1382 (10%) being current smokers and 532 (4%) having recently quit (within 6 months). Of the current smokers and those who had recently quit, all were advised to quit or to stay smoke-free, and 380 (20%) accepted referral to a sc counselling service. Conclusions: Here, we provide a comprehensive practice blueprint for the implementation of digitally based sc programs as a standard of care within comprehensive cancer centres with high patient volumes.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".