An Examination of Policies Addressing Resident Smoking in Retirement Homes in Ontario, Canada
Bibliographic record
Abstract
Ontario, Canada is currently the only province with legislation requiring the mandatory implementation of automatic sprinklers in retirement homes. Changes to the province’s fire code are a direct response to the 2009 presumed tobacco-related fire in a retirement home in Orillia, Ontario. In May 2013, the Ministry of Safety and Community Services announced significant amendments to the fire code, requiring all retirement homes in Ontario to install automatic sprinklers by 2018. While sprinklers are an important step in protecting residents in the event of a fire, prevention of tobacco-related fires can be realized when effective tobacco use policies are implemented that safeguard not only against fires but other health hazards as well (e.g. burns or exposure to environmental tobacco smoke to non-smokers.) The purpose of this study is to examine Ontario's retirement home tobacco policies for residents and to determine the impact of current legislation. In a random selection process of 700 retirement homes, this study examines tobacco-related policies using a rubric scoring system to rate the comprehensiveness of the policies. Although retirement homes, may indeed engage in practices that are more comprehensive than the policies portray, implementing policies and subsequent training to ensure compliance are a first step in safeguarding the needs of smokers and non-smokers alike.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 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".