Legislation governing tobacco use in Ontario’s retirement homes
Bibliographic record
Abstract
Legislation banning smoking in public places is a key component of comprehensive tobacco control programs, yet residential facilities for aging adults are often exempt from such legislation. In Ontario, Canada, provincial legislation does not comprehensively safeguard retirement homes' residents and staff from tobacco-related health and safety concerns. This study provides a descriptive analysis of municipal-level bylaws in order to begin understanding the regulatory context of tobacco use in retirement homes in the Province. A stratified random sample of retirement homes (n = 75) was selected. A rubric was developed highlighting various components that a model policy would include, to allow for the independent review of municipal-level bylaws governing these 75 homes. Results indicate that 75% of retirement homes were located in areas without municipal-level tobacco legislation that addressed retirement homes. The remaining 25% (n = 19 retirement homes) were governed by eight different municipal-level bylaws, all of which lacked in overall comprehensiveness. Amending Ontario's regulatory framework to eliminate loopholes and include retirement homes, as well as the creation and modification of municipal-level legislation, will aid in safeguarding smokers and nonsmokers from the dangers of tobacco-related risks, including secondhand smoke, fires, igniting cigarettes while connected to oxygen, burns to skin, and damage to clothing and property.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".