Exposure to secondhand smoke in vehicles among Canadian adolescents: Years after the adoption of smoke-free car laws
Bibliographic record
Abstract
Exposure to secondhand smoke (SHS) can result in several adverse health consequences. SHS concentrations in vehicles can significantly exceed levels present in other enclosed spaces. Years after the adoption of smoke-free car laws, this study examined the prevalence of exposure to SHS in vehicles among adolescents. Data were utilized from the 2016-2017 Canadian Student Tobacco, Alcohol and Drugs Survey (n = 48,444). The prevalence of exposure to SHS in cars was estimated by grade level and demographic characteristics. The results showed a gradient by grade level in exposure to SHS with students in upper-grade levels reporting a higher prevalence of SHS in cars. SHS varied by province, with the lowest rate found in British Columbia (15.6%) and the highest in Saskatchewan (36.9%). The provinces with laws that extend protections to older children also had high rates of SHS exposure among students in upper-grade levels. Students exposed to SHS were more likely to engage in risky behaviors, including the use of marijuana, alcohol, cigarettes, and e-cigarettes. Despite laws prohibiting smoking in vehicles carrying children, SHS prevalence remains high. While enforcement of these laws may be challenging, persuasion campaigns highlighting that children are especially vulnerable to the health risks of SHS may be beneficial.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".