Challenges and Adaptations for Providing Smoking Cessation for Patients with Cancer across Canada during the COVID-19 Pandemic
Bibliographic record
Abstract
Smoking cessation after a cancer diagnosis can improve health outcomes, but the Coronavirus disease 2019 (COVID-19) pandemic significantly altered healthcare patterns and strained resources, including for smoking cessation support for cancer patients. A Network that included all 13 provinces and territories (jurisdictions) in Canada received funding and coordinated support from a national organization to implement access to smoking cessation support in cancer care between 2016 and 2021, including throughout the COVID-19 pandemic. Descriptive analyses of meetings between the organization and jurisdictions between March of 2020 and August of 2021 demonstrated that all jurisdictions reported disruptions of existing smoking cessation approaches. Common challenges include staff redeployment, inability to deliver support in person, disruptions in travel, and loss of connections with other clinical resources. Common adaptations included budget and workflow adjustments, transition to virtual approaches, partnering with other community resources, and coupling awareness of the harms of smoking and COVID-19. All jurisdictions reported adaptations that maintained or improved access to smoking cessation services. Collectively, data suggest coordinated national efforts to address smoking cessation in cancer care could be crucial to maintaining access during an international healthcare crisis.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".