The Public Health Implications of Administrative Policy Responses to E-Cigarette Use: A Content Analysis of Ontario’s University Campus Policies
Bibliographic record
Abstract
Purpose: To investigate Ontario university’s institutional policy responses to the influx in e-cigarette use among adolescents and their associated regulation within campuses. Methods: A review of relevant policy documents among Ontario’s 22 public universities was conducted. A manual search yielded relevant documents which were subjected to individual analysis by the authors. Interpretation and thematic grouping of their respective policies on e-cigarette use was conducted. Results:17 of 22 universities (n=17) have published policy documents relating to e-cigarette use on campus. The remaining 5 universities did not have a publicly available e-cigarette policy or do not mention e-cigarettes within their policy on smoking. Discussion: 5 major thematic groups were identified, falling along a spectrum from blanket bans of e-cigarette use to the failure to reference e-cigarettes in relevant policy. Conclusion: Administrative policies regarding e-cigarette use among Ontario universities are heterogenous and vary dramatically in scope. Future research in the arena of e-cigarette regulation and harm prevention would be beneficial to health promotion on university campuses in Ontario. See complete issue to read the full text.
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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 | 0.027 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.016 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".