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Record W3152518996 · doi:10.12927/hcq.2021.26469

An Analysis of the Long-Term Care Policy Mandates Implemented in Canada during the First Wave of the COVID-19 Pandemic

2021· article· en· W3152518996 on OpenAlexaffvenueabout
Danielle Just, Caroline Variath

Bibliographic record

VenueHealthcare Quarterly · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsHumber PolytechnicCancer Care OntarioUniversity of Toronto
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakTerm (time)Vulnerability (computing)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Long-term careBest practiceThird wavePolitical scienceMedicinePublic administrationEconomic growthNursingVirologyEconomicsLawPolitical economyComputer security

Abstract

fetched live from OpenAlex

The first wave of the COVID-19 pandemic had a catastrophic impact on Canada's long-term care system. This report compares the experiences of the five Canadian provinces that were most affected by COVID-19. This includes a comparison of the five mandates that provincial governments implemented in order to protect long-term care residents. Findings from this report indicate that the Canadian provinces whose long-term care systems were most devastated by COVID-19 failed their residents by responding too slowly compared with provinces with similar long-term care systems. This lesson should guide policy reform to safeguard residents during the second wave and beyond.

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)0.003
metaresearch head score (Gemma)0.008
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.819
Threshold uncertainty score0.950

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0090.002
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.412
Teacher spread0.361 · 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

Citations5
Published2021
Admission routes3
Has abstractyes

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