Smoke-Free School Policy and Exposure to Secondhand Smoke: A Quasi-Experimental Analysis
Bibliographic record
Abstract
INTRODUCTION: Tobacco control prevention efforts are important to protect people from exposure to dangerous tobacco smoke, support cessation, and reduce tobacco-use initiation. While smoke-free laws have been a widespread tobacco control strategy, little work has been done to examine the impact of smoke-free school policies. The objective of this study is to evaluate the impact of provincial smoke-free school ground policies on youth-reported exposure to secondhand smoke (SHS) on school property. METHODS: This study used a nationally representative sample of 20 388 youth aged 15-18 from the 2005-2012 Canadian Tobacco Use Monitoring Survey. A quasi-experimental design was used to evaluate the impact of smoke-free school policies on SHS exposure. RESULTS: Approximately over half (52%) of respondents reported SHS exposure on a school property in the past month. Smoke-free school policy had a statistically significant effect on SHS exposure. Specifically, the adoption of smoke-free school reduced the probability of SHS exposure by about 8 percentage points. Respondents who were smokers were more likely to report being exposed to SHS than nonsmokers. Likewise, those living in urban areas had higher probability of being exposed to SHS than those living in rural parts of Canada. CONCLUSIONS: Reported exposure to tobacco smoke did decrease after the introduction of smoke-free ground policies; however, almost half of high-school aged youth report exposure in the last month. Across Canada, provincial health authorities as well as school administers may need to assess the implementation of these smoke-free policies and improve enforcement strategies to further reduce exposure to dangerous SHS.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".