Effect of smoke-free workplaces on smoking behaviour: systematic review
Bibliographic record
Abstract
OBJECTIVE: To quantify the effects of smoke-free workplaces on smoking in employees and compare these effects to those achieved through tax increases. DESIGN: Systematic review with a random effects meta-analysis. STUDY SELECTION: 26 studies on the effects of smoke-free workplaces. SETTING: Workplaces in the United States, Australia, Canada, and Germany. PARTICIPANTS: Employees in unrestricted and totally smoke-free workplaces. MAIN OUTCOME MEASURES: Daily cigarette consumption (per smoker and per employee) and smoking prevalence. RESULTS: Totally smoke-free workplaces are associated with reductions in prevalence of smoking of 3.8% (95% confidence interval 2.8% to 4.7%) and 3.1 (2.4 to 3.8) fewer cigarettes smoked per day per continuing smoker. Combination of the effects of reduced prevalence and lower consumption per continuing smoker yields a mean reduction of 1.3 cigarettes per day per employee, which corresponds to a relative reduction of 29%. To achieve similar reductions the tax on a pack of cigarettes would have to increase from $0.76 to $3.05 (0.78 euro to 3.14 euro) in the United States and from 3.44 pounds sterling to 6.59 pounds sterling (5.32 euro to 10.20 euro) in the United Kingdom. If all workplaces became smoke-free, consumption per capita in the entire population would drop by 4.5% in the United States and 7.6% in the United Kingdom, costing the tobacco industry $1.7 billion and 310 million pounds sterling annually in lost sales. To achieve similar reductions tax per pack would have to increase to $1.11 and 4.26 pounds sterling. CONCLUSIONS: Smoke-free workplaces not only protect non-smokers from the dangers of passive smoking, they also encourage smokers to quit or to reduce consumption.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".