Outdoor Smoking Ban at a Cancer Center
Bibliographic record
Abstract
Policies restricting indoor worksite tobacco use began being implemented more than a decade ago. More recently, the scope of these policies has been expanding to outdoors, with hospitals leading the trend in restricting smoking throughout their grounds. However, research on the effects such bans have on employees is scarce. The purpose of the current study was to examine the impact of a campus-wide smoking ban on employees and patients at a cancer center. Employees completed anonymous questionnaires during the months before (n = 607; 12% smokers) and 3 months after the ban implementation (n = 511; 10% smokers). Patients (n = 278; 23% smokers) completed an anonymous questionnaire preban. Results showed that 86% of nonsmokers, 20% of employees who smoke, and 57% of patients who smoke supported the ban. More than 70% of smokers were planning or thinking about quitting at both time points and nearly one-third were interested in cessation services following the ban. Before the ban, 32% expected the ban to have a negative effect on job performance and 41% thought their smoking before and after work would increase. Postban, 22% reported a negative impact on job performance, 35% increased smoking before and after work, and 7% quit. Overall, these data revealed an overwhelming support for an outdoor smoking ban by nonsmoker employees and patients. Although a majority of employee smokers opposed the ban, a significant proportion was interested in cessation. Compared with preban expectations, a lower proportion experienced negative effects postban. Findings suggest a need for worksite cessation programs to capitalize on the window of opportunity created by tobacco bans, while also addressing concerns about effects on work performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".