MétaCan
Menu
← Back to cohort
Record W4321204491 · doi:10.12927/hcq.2023.27017

One System, Multiple Hospitals: A Unified Paediatric Healthcare System Response to the COVID-19 Pandemic

2023· article· en· W4321204491 on OpenAlexaffvenue
Kayla Esser, Paul J. Davis, Bryn Badour, Kate Langrish, Judy Van Clieaf, Andrew Baker, Julia Orkin

Bibliographic record

VenueHealthcare Quarterly · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)MedicineHealthcare systemSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakHealth careMedical emergencyInfectious disease (medical specialty)VirologyDisease

Abstract

fetched live from OpenAlex

To address severe adult in-patient capacity pressures during the COVID-19 pandemic, 15 community hospitals were mandated to close their in-patient paediatric units (167 beds) and transfer paediatric in-patients to a single paediatric tertiary hospital. The tertiary hospital increased bed capacity through a surge plan activation, while community hospitals redeployed resources to fill the gaps in adult care. Also, 530 patients were transferred solely to increase adult bed capacity during the closure. Several factors enabled the system to function collaboratively. Closures increased the potential adult in-patient capacity by 6,740 bed days and demonstrated an unprecedented system-wide approach to the provision of integrated paediatric care across the region.

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)0.016
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0080.003
Open science0.0030.016
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0060.000

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 head0.096
GPT teacher head0.386
Teacher spread0.290 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueHealthcare Quarterly→Same topicCOVID-19 and healthcare impacts→French-language works237,207→