Thirdhand Smoke Exposure in Homes with Children under 48 Months during the First Wave of the COVID-19 Pandemic Confinement in Barcelona (Spain)
Bibliographic record
Abstract
Background/Objectives: Due to serious restrictions on mobility, some children might have increased exposure to THS due to home confinement. To characterize third-hand smoke (THS) exposure in children under 48 months at homes in Spain during the confinement of the first wave of COVID-19.Methods: Cross-sectional study of a non-probabilistic sample of parents (n = 311). The gathered information was about smoking status, second-hand smoke (SHS) exposure of their children, and voluntary regulation of tobacco consumption at their home. A variable of THS exposure at home was derived, classifying as ‘THS exposed’ those children whose parents reported living with a smoker or with smoking parents and non-exposed to SHS; ‘Non exposed’ children were, therefore, all other children.Results: Almost a quarter of the children (23.5%) were exposed to THS. This prevalence was significantly higher among those children whose parents increased tobacco consumption during confinement (40.5%), whose parents had lower or medium educational levels (42.9% and 41.7%), and with younger parents (24.8%). In contrast, the prevalence was significantly lower among those children living in homes with complete voluntary smoke restrictions (21.1%).Conclusions/Recommendations: To reduce THS exposure among children, it is important to work on information campaigns to raise awareness regarding THS exposure, promote recommendations to avoid exposure to THS, and develop legislation promoting smoke-free environments (in homes and vehicles).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".