“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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".