Protecting vulnerable groups from tobacco-related harm during and following the COVID-19 pandemic
Bibliographic record
Abstract
Marginalized populations are being disproportionally affected by the current pandemic. Direct effects include higher infection rates with greater morbidity and mortality; indirect effects stem from the societal response to limit the spread of the virus. These same groups also have smoking rates that are significantly higher than the general population. In this commentary, we discuss how the pandemic has been acting to further increase the harm from tobacco endured by these groups by applying the syndemic framework. Using this approach, we elaborate on the factors that promote clustering of harms from tobacco with harms from COVID-19. These include the worsening of psychological distress, a potential increase in smoking behaviour, greater exposure to second-hand smoke and less access to smoking cessation services. Then, we offer mitigation strategies to protect disadvantaged groups from tobacco-related harm during and following the COVID-19 pandemic. These strategies include affordable smoking cessation services, a proactive approach for smoking treatment using information technology, opportunistic screening and treatment of tobacco dependence among individuals presenting for COVID-19 vaccination, policy interventions for universal coverage of cessation pharmacotherapy, comprehensive smoke-free policies and regulation of tobacco retail density. Now more than ever, coordinated action between clinicians, health care systems, public health organizations and health policy makers is needed to protect vulnerable groups from the harm of tobacco.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".