An evaluation of school-based e-cigarette control policies’impact on the use of vaping products
Bibliographic record
Abstract
INTRODUCTION: Electronic cigarette (e-cigarette) use among youth is common, and so efforts to regulate its use and availability are continually being made. The school environment represents an important domain for advancing health policy among youth populations. This study examines the impact of school-based e-cigarette control policies on student e-cigarette use in the context of a natural experiment. METHODS: Using three years of longitudinal student and school level data (2013/2014 to 2015/2016), from a sample of 69 secondary schools in Ontario, Canada, a generalized estimating equation approach examined the impact of school-based e-cigarette control policy changes on the prevalence of youth e-cigarette use. The main outcome of interest was current e-cigarette use, while covariates included age, gender, ethnicity, and amount of spending money in dollars per week the student has. Tests of proportion (t-tests) were used to examine whether there were any significant differences in the changes for each intervention school relative to the sample of schools that report no changes in school-level e-cigarette control policies. RESULTS: Estimates from the generalized estimating equation approach suggest that students had lower odds of using e-cigarettes in schools where an e-cigarette control policy was implemented. That is, the e-cigarette control policy decreased the adjusted odds of being an e-cigarette user (OR=0.68; 95% CI: 0.48-0.97). Examining school-specific impact, at four of six schools that had an e-cigarette control policy, the ban on the use of e-cigarettes may have lowered the prevalence of e-cigarette use. CONCLUSIONS: This is the first study to use longitudinal data to study school-level e-cigarette use and the impact of e-cigarette control policy. These results provide new evidence that school-level policies banning the use of e-cigarettes on school property may be effective in reducing e-cigarette use (or preventing it) in their current form, as seen in this natural experiment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".