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Record W3188770941 · doi:10.19044/esj.2021.v17n23p1

Long-Term Care Regulations in Ontario, Canada during COVID-19

2021· article· en· W3188770941 on OpenAlexaffabout
Steve Hunt, Elena Hunt

Bibliographic record

VenueEuropean Scientific Journal ESJ · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsLaurentian UniversityLakehead UniversityRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsStatutory lawCoronavirus disease 2019 (COVID-19)Term (time)Health careDescriptive statisticsLawPopulationPolitical scienceMedicineEnvironmental healthStatistics

Abstract

fetched live from OpenAlex

The Ontario, Canada statutory requirements of Nurse Patient ratios and other Health care professionals’ activities are discussed following a descriptive, analytical and investigative approach. Using OECD Statistics and evaluating the impact on population health during COVID-19, as well as drawing from constitutional law and administrative law, the authors apply, explain and clarify opinions of jurists, rulings of judges and arbitrators and pull comparisons to statutory and statistical data from international jurisdictions such as Australia and USA. Recommendations for improvement of the Ontario Health Care system are conferred.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.062
GPT teacher head0.264
Teacher spread0.202 · 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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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