Identifying Best Implementation Practices for Smoking Cessation in Complex Cancer Settings
Bibliographic record
Abstract
BACKGROUND: In response to evidence about the health benefits of smoking cessation at time of cancer diagnosis, Ontario Health (Cancer Care Ontario) (OH-CCO) instructed Regional Cancer Centres (RCC) to implement smoking cessation interventions (SCI). RCCs were given flexibility to implement SCIs according to their context but were required to screen new patients for tobacco status, advise patients about the importance of quitting, and refer patients to cessation supports. The purpose of this evaluation was to identify practices that influenced successful implementation across RCCs. METHODS: A realist evaluation approach was employed. Realist evaluations examine how underlying processes of an intervention (mechanisms) in specific settings (contexts) interact to produce results (outcomes). A realist evaluation may thus help to generate an understanding of what may or may not work across contexts. RESULTS: The RCCs with the highest Tobacco Screening Rates used a centralized system. Regarding the process for advising and referring, three RCCs offered robust smoking cessation training, resulting in advice and referral rates between 80% and 100%. Five RCCs surpassed the target for Accepted Referral Rates; acceptance rates for internal referral were highest overall. CONCLUSION: Findings highlight factors that may influence successful SCI implementation.
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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 | 0.082 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".