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Record W3112684333 · doi:10.1503/cmaj.202636

The case for relaxing no-visitor policies in hospitals during the ongoing COVID-19 pandemic

2020· article· en· W3112684333 on OpenAlexafffundvenueabout
Laveena Munshi, Gerald A. Evans, Fahad Razak

Bibliographic record

VenueCanadian Medical Association Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsKingston Health Sciences CentreUniversity of TorontoInstitute of Health Services and Policy ResearchUniversity Health NetworkSt. Michael's Hospital
FundersUniversity of Toronto
KeywordsCoronavirus disease 2019 (COVID-19)Visitor patternPandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Transmission (telecommunications)BetacoronavirusCoronavirus InfectionsMedicineMedical emergencyCoronavirusComputer scienceVirologyTelecommunicationsDiseaseInfectious disease (medical specialty)Pathology

Abstract

fetched live from OpenAlex

KEY POINTS In an attempt to mitigate excess transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Canadian hospitals adopted “no visitor” policies during the first wave of the pandemic. A reflexive and almost complete restriction of visitors occurred across all hospitals

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)0.003
metaresearch head score (Gemma)0.119
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.119
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.051
GPT teacher head0.365
Teacher spread0.314 · 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
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

Citations70
Published2020
Admission routes4
Has abstractyes

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