Sustainability of Tobacco Treatment Programs in the Cancer Center Cessation Initiative
Bibliographic record
Abstract
The NCI's Cancer Center Cessation Initiative (C3I) has a specific objective of helping cancer centers develop and implement sustainable programs to routinely address tobacco cessation with patients. Sustaining tobacco treatment programs requires the maintenance of (1) core program components, (2) ongoing implementation strategies, and (3) program outcomes evaluation. NCI funding of C3I included a commitment of resources toward sustainability. This article presents case studies to illustrate key strategies in developing sustainability capacity across 4 C3I-funded sites. Case studies are organized according to the domains of sustainability capacity defined in the Clinical Sustainability Assessment Tool (CSAT). We also describe the C3I Sustainability Working Group agenda to make scientific and practical contributions in 3 areas: (1) demonstrating the value of tobacco use treatment in cancer care, (2) identifying implementation strategies to support sustainability, and (3) providing evidence to inform policy changes that support the prioritization and financing of tobacco use treatment. By advancing this agenda, the Sustainability Working Group can play an active role in advancing and disseminating knowledge for tobacco treatment program sustainability to assist cancer care organizations in addressing tobacco use by patients with cancer within and beyond C3I.
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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 | 0.044 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| 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".