Implementing a 3As and ‘Opt-Out’ Tobacco Cessation Framework in an Outpatient Oncology Setting
Bibliographic record
Abstract
Tobacco cessation has been recognized as an important goal for all ambulatory cancer centres to provide the best possible treatment outcomes and quality of life. However, cessation interventions are applied inconsistently in this setting, with less than one-half of tobacco users being offered evidence-based interventions. The 'opt-in' approach traditionally used in cessation, which targets patients who feel ready to quit, may limit the number of patients who are able to receive treatment, and evidence suggests that tobacco users quit at the same rate regardless of their perceived readiness. This paper reports the results of implementing a tobacco cessation framework utilizing the 3As and an 'opt-out' approach as a standard of cancer care at a Regional Cancer Centre. A comparison of data from 2017-2018 and 2018-2019 demonstrated an increase in the number of patients screened for tobacco use (76.9% to 90.1%, respectively), and in the number of accepted referrals to quit support (11.5% to 34.7%, respectively). The revised framework was effective at improving referral acceptance rates, despite tobacco use rates remaining stable among the two groups. This demonstrates that employing the 'opt-out' approach is a more effective strategy to connect patients with the smoking cessation supports required to optimize their cancer care.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".