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Record W1835862617 · doi:10.1136/bmj.h3931

Improving hand hygiene in hospitals—more is better

2015· letter· en· W1835862617 on OpenAlexaff
Matthew Muller

Bibliographic record

VenueBMJ · 2015
Typeletter
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsHygienePsychological interventionMedicineSystematic reviewHealth careMEDLINEFamily medicineNursing

Abstract

fetched live from OpenAlex

The WHO-5 bundle is a good place to start, but might work better with optional extras Hand hygiene, performed appropriately by healthcare workers, protects patients and providers from infections acquired in hospital; ultimately, good hand hygiene saves lives. Despite this, adherence to hand hygiene guidelines is unacceptably poor.1 Hospitals worldwide need to tackle this critical patient safety issue. Many hospitals have tried to improve hand hygiene. Few have been unable to achieve sustained improvements. In a linked paper, Luangasanatip and colleagues (doi:10.1136/bmj.h3728) sought to identify interventions that improve compliance with good hand hygiene in hospital and to establish their relative efficacy through a systematic review and network meta-analysis.2 They excluded study designs at high risk of bias (such as uncontrolled before-after studies). The authors conducted a thorough literature review that identified studies published in 1980-2014. Their review included 41 studies that met eligibility criteria, 31 of which were published after 2009, including five of the six included randomised controlled trials. Most of the evaluated interventions were “multi-modal” or “bundled” interventions that included several different components; only six studies …

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.026
GPT teacher head0.329
Teacher spread0.303 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations4
Published2015
Admission routes1
Has abstractyes

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