“We Are Watching”: The effectiveness of implementing novel anti-smoking signage on hospital property
Bibliographic record
Abstract
Objective: Poor adherence to smoke-free policies on hospital property is an ongoing challenge. This study introduced novel anti-smoking signage onto hospital property with the aim of evaluating its effectiveness on reducing the incidence of smoking in designated areas.Methods: This prospective ecological study used cigarette butt count as a proxy to measure smoking prevalence at a single hospital’s three exit sites between October–December 2013. A pre-analysis of cigarette butt count at each site was conducted and the site with the highest count was selected for intervention; the two remaining sites were controls. The intervention signs featured a pair of stern male eyes with a forward gaze with “Don’t Smoke” written in black font and “We Are Watching” in red font below. Pre- and post-intervention cigarette butt counts were collected over 18 days and 14 days respectively. Climate was included in the analysis.Results: The number of cigarette butts decreased at the intervention site across 11 of the 14 post- intervention monitored days (29.8% decrease). Cigarette butt counts increased across both control sites (32.9% and 58.8%). One-way ANOVA revealed a significant interaction (p = .000) between location and pre-/post-intervention periods. A two-way ANOVA evaluating location, intervention period, and climate temperature change (± 10 degrees Celsius) revealed statistical significance (p < .05). Interaction between location and climate was not significant.Conclusions: This study demonstrated a decrease in cigarette butts at the hospital exit where the “watching eyes” signs were implemented. Simple, low-cost anti-smoking interventions such as this may assist in creating healthier, smoke-free environments on hospital properties.
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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 | 0.003 | 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 | 0.000 |
| 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".