Implementation of a Comprehensive Hospital-Based Smoking-Cessation Program in Cancer
Bibliographic record
Abstract
Background and context: Quitting smoking after a cancer diagnosis minimizes treatment-related effects, improves prognosis and enhances quality of life. However, smoking cessation has yet to be integrated as a standard component of cancer care and cessation programs are underused. Aim: To address this gap between evidence and practice, the Princess Margaret Cancer Centre in Toronto, Canada sought to implement a comprehensive, evidence-based program that would introduce smoking cessation–related screening, referrals and education as standard practice in cancer care. Strategy/Tactics: We adapted the Framework for Managing eHealth Change to guide successful implementation of a Smoking Cessation Program (SCP) utilizing 6 components: 1) Leadership and governance 2) Stakeholder engagement and partnerships 3) Communication 4) Patient and provider education 5) Analysis and clinical integration 6) Monitoring and evaluation of program performance metrics. Program/Policy process: The SCP designed, tested and implemented a multilingual e-referral system (CEASE) to screen patients for smoking status, provide education and advice on quitting, and generate an electronic referral to cessation programs. Partnerships were established with 3 smoking-cessation programs to offer patients a breadth of services tailored to individual needs, preferences and medical history. Patient and provider education was developed to address the unique knowledge gaps, beliefs and stigma associated with smoking and a cancer diagnosis. Outcomes: Program metrics indicate that of 11,366 new patients eligible for screening between April 2017 to March 2018, 62% (n = 6629) were screened with 10% (n = 655) identifying as current smokers and 4% (n = 261) as recent quitters (6 months or less). Of smokers and recent quitters, 21% (n = 170) accepted a referral to a smoking-cessation program. What was learned: The implementation of the SCP presents a comprehensive blueprint to establish a smoking-cessation program as a standard of quality care. Elements of the SCP can be adapted to local, regional and national contexts. Future directions include assessing strategies to increase screening and referral rates, collection of long-term outcomes, and integration into the patient portal.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".