Reduction in staff smoking rates in North Coast Area Health Service, NSW, following the introduction of a smoke-free workplace policy
Bibliographic record
Abstract
AIM: To evaluate changes in staff smoking rates following the implementation of Smoke Free Health Care, an innovative, change-management process that introduced a smoke-free workplace policy in the North Coast Area Health Service of NSW. METHODS: Survey questionnaires were sent to all staff before and after the introduction of the policy. Return rates were 17.3% (690/3988) in 1999 and 25.4% (2012/7921) in 2007. Chi-square tests and multivariate logistic regression analysis were used to determine differences. RESULTS: Staff smoking rates decreased significantly from 22.3% to 11.8% (p<0.0001). Smoking rates in 1999 were not significantly different to the state population's (22.3% and 24.1%, p=0.3), but were significantly different in 2007 (11.8% and 20.1%, p<0.0001). Over a quarter (27.6%) of staff who smoked when implementation began quit smoking; more than twice the rate before implementation (12%, p<0.0001). CONCLUSION: These changes in staff smoking rates indicate the effectiveness of a comprehensive change-management approach to implementing smoke-free workplace policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".