People who smoke and formerly smoked do support a smoking ban in open spaces during and after the COVID-19 pandemic in Spain
Bibliographic record
Abstract
BACKGROUND: Spain implemented an extensive outdoor smoking ban during the COVID-19 pandemic in 2020. We examined support for this restriction during and beyond the pandemic among people who currently and formerly smoked. METHODS: Cross-sectional study. The 2021 ITC EUREST-PLUS Spain Survey used a multistage sampling to obtain a representative sample of people who currently or formerly smoked (n=1006). We estimated prevalence ratios (PRs) to examine associations with ban support during and after the pandemic. Analyses accounted for the complex sampling design and were weighted to ensure representativeness. RESULTS: Most people who currently (79.2%) and formerly smoked (94.6%) supported the outdoor smoking ban when a safe interpersonal distance could not be maintained during the pandemic. Support for a permanent ban remained substantial (61.4% and 87.2%, respectively). Those who currently smoke were more likely to support bans during and after the pandemic if they had smoke-free homes (PR=1.11; PR=1.29, respectively), understood secondhand smoke harms to health (PR=1.40; PR=1.65), had tried to quit (PR=1.14; PR=1.30) and self-reported as healthy (PR=1.40; PR=1.47). Those who formerly smoked were more likely to support bans during the pandemic if they had smoke-free homes (PR=1.08) and did not self-report as healthy (PR=0.95); and after the pandemic if they had quit smoking for ≥6 months (PR=1.23) and used alternative tobacco products (PR=1.17). CONCLUSIONS: Most people who currently or formerly smoked in Spain supported the outdoor smoking ban during and after the COVID-19 pandemic. This high acceptance suggests that the pandemic may have created an opportunity to strengthen tobacco control.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".