Are Canadian hospitals leading by example to promote smoke-free hospital properties? Rationale, challenges and opportunities
Bibliographic record
Abstract
Knowing the devastation of tobacco use and the evidence to support proven tobacco reduction approaches, such as smoke-free hospital property policies, are Canadian hospitals doing all they can to lead by example? This paper explores the background and diverse views on smoke-free hospital properties to illuminate the rationale, challenges and opportunities of this important healthcare initiative. Currently, some hospitals in Canada have transitioned to smoke-free properties; however, many still allow smoking in designated areas or do not have any policies in place. Fear, speculation and reservations around compliance, leadership, negative perceptions, safety and patient care are some of the reasons that appear to be stalling progress in many healthcare facilities; nevertheless, the evidence supporting the implementation of comprehensive smoke-free hospital property policies far outweighs the concerns. Key considerations for successful policy implementation include: leadership and enforcement; systematic tobacco dependence treatment; and elimination of designated smoking areas (DSA’s) and policy exclusions. Hospitals are ideal institutions to continue the downward trend in tobacco use prevalence. Through smoke-free property policies, Canadian hospitals can make a significant impact and lead by example in their communities by creating opportunities to promote healthy choices, protecting individuals from exposure to environmental tobacco smoke (ETS), supporting those who are trying to quit or who have quit smoking and by sending a clear message that smoking and exposure to tobacco smoke is harmful. As witnessed though the learnings of leading hospitals, transitioning to a smoke-free hospital property is achievable.
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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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".