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Record W2107075500 · doi:10.1016/j.ajic.2014.04.017

A study of the efficacy of flashing lights to increase the salience of alcohol-gel dispensers for improving hand hygiene compliance

2014· article· en· W2107075500 on OpenAlexaff
Gianni D’Egidio, Rakesh Patel, Babak Rashidi, M Mansour, Elham Sabri, Paul Milgram

Bibliographic record

VenueAmerican Journal of Infection Control · 2014
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsHygieneMedicineFlashingCompliance (psychology)Medical emergencySocial psychologyPsychology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.326
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2014
Admission routes1
Has abstractyes

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