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Record W2934142552 · doi:10.1016/j.jhin.2019.03.014

Improving healthcare worker adherence to the use of transmission-based precautions through application of human factors design: a prospective multi-centre study

2019· article· en· W2934142552 on OpenAlexafffund
Victoria R. Williams, Jerome A. Leis, Patricia Trbovich, Tanya Agnihotri, Won‐Young Lee, Bobby Joseph, L. Glen, Melisa Avaness, Fatema Jinnah, Natasha Salt, Jeff Powis

Bibliographic record

VenueJournal of Hospital Infection · 2019
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsToronto East General HospitalUniversity of TorontoNorth York General HospitalSunnybrook Health Science CentreHealth Sciences Centre
FundersToronto East General Hospital Foundation
KeywordsMedicineSignagePersonal protective equipmentHealth careHealthcare workerTransmission (telecommunications)Intervention (counseling)Medical emergencyNursingCoronavirus disease 2019 (COVID-19)DiseaseAdvertisingPathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

A key component of transmission-based precautions (TBPs) is the use of personal protective equipment (PPE) but healthcare worker (HCW) adherence remains suboptimal. A human factors-based intervention was implemented to improve adherence to TBPs including (i) improved signage, (ii) standardized placement of signage, (iii) introduction of a mask with integrated face shield, and (iv) improvement in PPE availability. Donning of the correct PPE by HCWs improved significantly (79.7 vs 56.4%; P < 0.001). This approach may be more effective than education alone, but further study is required to determine sustainability and subsequent impact on transmission of healthcare-associated infections.

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)0.000
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 score0.155
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.077
GPT teacher head0.359
Teacher spread0.282 · 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

Citations27
Published2019
Admission routes2
Has abstractyes

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