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Record W2153547086 · doi:10.1111/acem.12754

Hand Hygiene Compliance in an Emergency Department: The Effect of Crowding

2015· article· en· W2153547086 on OpenAlexaffabout
Matthew Muller, Eileen Carter, Naureen Siddiqui, Elaine Larson

Bibliographic record

VenueAcademic Emergency Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineHygieneCrowdingEmergency departmentMultivariate analysisHealth careCompliance (psychology)Emergency medicineMedical emergencyFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Emergency department (ED) crowding results from the need to see high volumes of patients of variable acuity within a limited physical space. ED crowding has been associated with poor patient outcomes and increased mortality. The authors evaluated whether ED crowding is also associated with reduced hand hygiene compliance among health care workers. METHODS: A trained observer measured hand hygiene compliance using standardized definitions for 22 months in the 40-bed ED of a 475-bed academic hospital in Toronto, Ontario, Canada. ED crowding measures, including mean daily patient volumes, time to initial physician assessment, and daily nursing hours, were obtained from hospital administrative and human resource databases. Known predictors of hand hygiene compliance, including the indication for hand hygiene and the health care workers' professions, were also measured. Hand hygiene data, measured during 20-minute observation sessions, were linked to aggregate daily results for each crowding metric. Crowding metrics and known predictors of hand hygiene compliance were then included in a multivariate model if associated with hand hygiene compliance at a p-value of <0.20. RESULTS: Hand hygiene compliance was 29% (325 of 1,116 opportunities). Alcohol-based hand rinse was used 66% of the time. Nurses accounted for 68% of hand hygiene opportunities and physicians for 18%, with the remaining 14% attributed to nonphysician, nonnurse health care workers. The most common indications for hand hygiene were hand hygiene prior to (35%) and hand hygiene following (52%) contact with the patient or his or her environment. In multivariate analysis, time to physician assessment > 1.5 hours was associated with lower compliance (odds ratio [OR] = 0.67, 95% confidence interval [CI] = 0.51 to 0.89). Additionally, compliance was lower for nonnurse, nonphysician health care workers (OR = 0.51, 95% CI = 0.33 to 0.79) and higher for hand hygiene performed after contact with the patients or his/her environment, compared to hand hygiene performed before contact with the patient or his/her environment (OR = 2.0, 95% CI = 1.5 to 2.7). Daily patient volumes and nursing hours were not associated with hand hygiene compliance. CONCLUSIONS: ED hand hygiene compliance was low. Increased time to physician assessment was associated with reduced compliance, suggesting an association between crowding and compliance. Strategies that minimize ED crowding may improve ED hand hygiene compliance.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.125
GPT teacher head0.433
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), 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

Citations69
Published2015
Admission routes2
Has abstractyes

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