A study of the efficacy of flashing lights to increase the salience of alcohol-gel dispensers for improving hand hygiene compliance
Bibliographic record
Abstract
BACKGROUND: Many interventions have been implemented to improve hand hygiene compliance, each with varying effects and monetary costs. Although some previous studies have addressed the issue of conspicuousness, we found only 1 study that considered improving hand hygiene by using flashing lights. METHOD: Our attention theory-based hypothesis tested whether a simple red light flashing at 2-3 Hz affixed to the alcohol gel dispensers, within the main hospital entrance, would increase hand hygiene compliance over the baseline rate. Baseline and intervention observations were completed over five 60-minute periods (Monday-Friday) from 7:30 to 8:30 AM using a covert observation method. RESULTS: Baseline hand hygiene compliance was 12.4%. Our intervention increased compliance to 23.5% during cold weather and 27.1% during warm weather. Overall, our pooled compliance rate increased to 25.3% (P < .0001). CONCLUSIONS: A simple, inexpensive flashing red light affixed to alcohol gel dispensers was sufficiently salient to approximately double overall hand hygiene compliance within the main hospital entrance. We hypothesize that our intervention drew attention to the dispensers, which then reminded employees and visitors alike to wash their hands. Compliance was worse during cold days, presumably related to more individuals wearing gloves.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".