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Record W3025087996 · doi:10.1503/cmaj.201017

Continuing care and COVID-19: a Canadian tragedy that must not be allowed to happen again

2020· article· en· W3025087996 on OpenAlexaffvenueabout
Jayna Holroyd‐Leduc, Andreas Laupacis

Bibliographic record

VenueCanadian Medical Association Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Continuing careTragedy (event)MedicineLong-term careHealth careNursingBetacoronavirusNursing homesContinuing educationGerontologyDiseaseOutbreakVirologyPolitical scienceInfectious disease (medical specialty)PathologyMedical education

Abstract

fetched live from OpenAlex

Recent estimates suggest that between 62% and 82% of deaths due to coronavirus disease 2019 (COVID-19) in Canada have occurred among residents of continuing care homes (which include nursing homes, long-term care and supportive living facilities).[1][1],[2][2] In Ontario, 5 of the 7 health care

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)0.009
metaresearch head score (Gemma)0.024
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.098
Threshold uncertainty score0.708

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0270.010
Scholarly communication0.0120.008
Open science0.0040.008
Research integrity0.0100.023
Insufficient payload (model declined to judge)0.0240.003

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 head0.040
GPT teacher head0.345
Teacher spread0.306 · 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
GenreCommentary

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

Citations68
Published2020
Admission routes3
Has abstractyes

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