Involving Family and Social Support Systems in Tobacco Cessation Treatment for Patients With Cancer
Bibliographic record
Abstract
Individuals from the family and social support network of patients with cancer can have a pivotal role in reinforcing patients' efforts to become and remain tobacco-free. This support is critical along the entire continuum of cancer care. Although NCI-designated Cancer Centers across the United States are increasingly offering tobacco cessation services as a result of the NCI Cancer Center Cessation Initiative (C3I), engaging patients' family and other support network in tobacco treatment is not yet a routine practice. To facilitate the consideration and involvement of patients' social support systems (including family, peers, and non-healthcare provider caregivers), we formed the C3I Family and Social Support Systems Working Group. This paper describes the current practices and challenges among C3I cancer centers centers in engaging the support systems of patients with cancer in order to reduce tobacco use and/or secondhand smoke exposure. Building on this knowledge, this Working Group proposes a research agenda to facilitate support persons' involvement in tobacco treatment as part of oncology care. The research priorities identified include establishing (1) evidence-based strategies for engaging family and social support systems in patients' cessation efforts, (2) interventions to provide cessation treatment options to support persons, and (3) best practices to routinely identify and engage family and social support systems in patients' cessation efforts.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".