Telehealth Delivery of Tobacco Cessation Treatment in Cancer Care: An Ongoing Innovation Accelerated by the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic precipitated a rapid transformation in healthcare delivery. Ambulatory care abruptly shifted from in-person to telehealth visits with providers using digital video and audio tools to reach patients at home. Advantages to telehealth care include enhanced patient convenience and provider efficiencies, but financial, geographic, privacy, and access barriers to telehealth also exist. These are disproportionately greater for older adults and for those in rural areas, low-income communities, and communities of color, threatening to worsen preexisting disparities in tobacco use and health. Pandemic-associated regulatory changes regarding privacy and billing allowed many Cancer Center Cessation Initiative (C3I) programs in NCI-designated Cancer Centers to start or expand video-based telehealth care. Using 3 C3I programs as examples, we describe the methods used to shift to telehealth delivery. Although telephone-delivered treatment was already a core tobacco treatment modality with a robust evidence base, little research has yet compared the effectiveness of tobacco cessation treatment delivery by video versus phone or in-person modalities. Video-delivery has shown greater medication adherence, higher patient satisfaction, and better retention in care than phone-based delivery, and may improve cessation outcomes. We outline key questions for further investigation to advance telehealth for tobacco cessation treatment in 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".