Factors Associated With Success of Smoke-Free Initiatives in Australian Psychiatric Inpatient Units
Bibliographic record
Abstract
OBJECTIVE: Smoking is the largest cause of preventable illness in the United States, the United Kingdom, Canada, Australia, and many other countries. Smokers with mental illness smoke significantly more than those without mental illness and therefore experience even greater smoke-related harm. Internationally, there is increasing pressure on psychiatric inpatient settings to adopt smoke-free policies. This study examined smoke-free policies across psychiatric inpatient settings in Australia and thereby identified factors that may contribute to the success or failure of smoke-free initiatives in order to better inform best practice in this important area. METHODS: Semistructured in-depth telephone interviews were conducted with 60 senior administrators and clinical staff with direct day-to-day experience with smoking activities in 99 adult psychiatric inpatient settings across Australia. Quantitative data were analyzed using descriptive statistical analysis and Pearson's chi square correlations measure of association. RESULTS: Factors associated with greater success of smoke-free initiatives were clear, consistent, and visible leadership; cohesive teamwork; extensive training opportunities for clinical staff; fewer staff smokers; adequate planning time; effective use of nicotine replacement therapies; and consistent enforcement of a smoke-free policy. CONCLUSIONS: A smoke-free policy is possible within psychiatric inpatient settings, but a number of core interlinking features are important for success and ongoing sustainability.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".