MétaCan
Menu
Back to cohort
Record W3096808450 · doi:10.1017/ice.2020.490

Infection Prevention and Control for 2019 Novel Coronavirus (2019 nCoV) in Acute Healthcare Settings: The Canadian Response

2020· article· en· W3096808450 on OpenAlexaffabout
Toju Ogunremi, Kathleen Dunn, Jennie Johnstone, Joanne Embreé

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2020
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsHealth Sciences CentrePublic Health OntarioPublic Health Agency of Canada
Fundersnot available
KeywordsInterimInfection controlHealth carePublic healthGuidelineGovernment (linguistics)MedicineContext (archaeology)Transmission (telecommunications)Environmental healthNursingIntensive care medicinePolitical scienceGeography

Abstract

fetched live from OpenAlex

Background: Severe acute respiratory coronavirus virus 2 (SARS-CoV-2), able to cause pneumonia in humans, was discovered in Wuhan, Hubei Province, China. Investigations related to transmissibility are ongoing, but human-to-human transmission involving healthcare workers providing patient care and close contacts of infected patients have been confirmed. Infection control procedures are necessary to prevent transmission during delivery of health care in healthcare settings. Public health in Canada is a shared responsibility among municipal, provincial, territorial, and federal governments. Significant public health events require coordination between all levels of government and a consistent approach across jurisdictions. The objective of this summary is to describe the Public Health Agency (PHAC)’s Infection Prevention and Control (IPC) guideline on SARS-CoV-2. Methods: The PHAC’s interim guideline for infection prevention and control of 2019-nCoV in acute healthcare settings was informed by the currently limited evidence available, and adapted to the context of healthcare delivery in Canada. The guideline is based upon Canadian guidance developed for previous coronavirus outbreaks (eg, SARS and MERS), as well as the World Health Organization (WHO)’s interim guidance. Technical advice was provided by the National Advisory Committee on Infection Prevention and Control (NAC-IPC) of the Government of Canada. Interjurisdictional collaboration and decision making between multiple authorities and levels of government was facilitated using PHACs federal/provincial/territorial (FPT) Public Health Response Plan for Biological events (Fig. 1). Results: In the absence of effective drugs or vaccines, IPC strategies to prevent or limit SARS-CoV-2 transmission in healthcare settings include the following: prompt identification of signs, symptoms and exposure criteria, implementation of appropriate IPC measures (eg, contact and droplet precautions, patient isolation, N95 respirator plus eye protection when performing aerosol-generating medical procedures on a person under investigation), and etiologic diagnosis. Guideline recommendations are informed by collective expert interpretation of available evidence. Recommendations cover all relevant areas including screening and assessment, public health surveillance and notification, laboratory testing and reporting, respiratory hygiene, hand hygiene, patient placement and flow, management of visitors, use of personal protective equipment, environmental cleaning and discontinuation of precautions. Conclusions: This guideline is an ever-changing document. Changes in recommendations provided may be warranted with new evidence, changes in WHO guidelines, or other identified concerns. FPT governments continue to work collaboratively to ensure that Canada is ready to respond to public health events and is prepared to protect the health of Canadians. Opportunities for international collaboration on IPC products, as well as knowledge exchange and mobilization, continue to thrive. Funding: None Disclosures: None

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)0.019
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0050.004
Scholarly communication0.0050.002
Open science0.0060.005
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0050.001

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 head0.065
GPT teacher head0.413
Teacher spread0.348 · 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 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

Citations5
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueInfection Control and Hospital EpidemiologySame topicDisaster Response and ManagementFrench-language works237,207