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Record W3185896660 · doi:10.1097/ncq.0000000000000584

Through Their Eyes

2021· article· en· W3185896660 on OpenAlexaff
Riley Moore, Alexandra Hayward, Kellee Necaise

Bibliographic record

VenueJournal of Nursing Care Quality · 2021
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsBoise Cascade (Canada)
Fundersnot available
KeywordsPersonal protective equipmentMedicinePandemicCoronavirus disease 2019 (COVID-19)Isolation (microbiology)Economic shortageHealth careSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Compliance (psychology)2019-20 coronavirus outbreakInfection controlMedical emergencyEmergency medicineDiseaseIntensive care medicineInfectious disease (medical specialty)VirologyOutbreakPsychologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Personal protective equipment (PPE) plays a critical role in protecting health care workers (HCWs). During the coronavirus disease-2019 (COVID-19) pandemic, shortages of PPE supplies drastically changed the way PPE was obtained and used by HCWs. PURPOSE: The objective was to investigate the impact of the COVID-19 pandemic and patient isolation type on PPE compliance. METHODS: This investigation was a survey of HCWs at a level 1 trauma teaching hospital regarding PPE compliance patterns prior to and during the COVID-19 pandemic. RESULTS: HCWs reported an increase in PPE compliance during the COVID-19 pandemic. Nearly half (48.6%) of respondents reported that isolation type impacted the decision to wear PPE, of which most were likely to forgo PPE with contact precautions. CONCLUSIONS: HCWs identified multiple barriers to compliance. The underutilization of PPE with contact precautions suggests that the risk of exposure is interpreted as low, and this could be a future target of education.

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)0.001
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.371
Threshold uncertainty score0.897

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.3710.197

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 head0.155
GPT teacher head0.499
Teacher spread0.344 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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