A qualitative investigation of smoke-free policies on hospital property
Bibliographic record
Abstract
BACKGROUND: Many hospitals have adopted smoke-free policies on their property. We examined the consequences of such polices at two Canadian tertiary acute-care hospitals. METHODS: We conducted a qualitative study using ethnographic techniques over a six-month period. Participants (n=186) shared their perspectives on and experiences with tobacco dependence and managing the use of tobacco, as well as their impressions of the smoke-free policy. We interviewed inpatients individually from eight wards (n=82), key policy-makers (n=9) and support staff (n=14) and held 16 focus groups with health care providers and ward staff (n=81). We also reviewed ward documents relating to tobacco dependence and looked at smoking-related activities on hospital property. RESULTS: Noncompliance with the policy and exposure to secondhand smoke were ongoing concerns. Peoples' impressions of the use of tobacco varied, including divergent opinions as to whether such use was a bad habit or an addiction. Treatment for tobacco dependence and the management of symptoms of withdrawal were offered inconsistently. Participants voiced concerns over patient safety and leaving the ward to smoke. INTERPRETATION: Policies mandating smoke-free hospital property have important consequences beyond noncompliance, including concerns over patient safety and disruptions to care. Without adequately available and accessible support for withdrawal from tobacco, patients will continue to face personal risk when they leave hospital property to smoke.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".