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Record W2798121841 · doi:10.29173/alr1268

Quarantine and the Law: The 2003 SARS Experience in Canada (A New Disease Calls on Old Public Health Tools)

2005· article· en· W2798121841 on OpenAlexvenueaboutno aff
Nola M. Ries

Bibliographic record

VenueAlberta Law Review · 2005
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
Fundersnot available
KeywordsQuarantinePreparednessPublic healthLegislatureLegislative historyOutbreakLawPandemicInfectious disease (medical specialty)Political sciencePublic administrationBusinessEnvironmental healthDiseaseMedicineCoronavirus disease 2019 (COVID-19)VirologyNursing

Abstract

fetched live from OpenAlex

The authority to quarantine individuals was tested by the 2003 global outbreak of SARS. Quarantine was used during that lime as a public health intervention tool to attempt to control the disease in Toronto. The outbreak put the public health preparedness of the Ontario legal system to the test. This article examines the legal issues related to the use of quarantine as a tool to control infectious disease outbreaks using the Ontario SARS epidemic as a case study. The author first analyzes the laws authorizing public health officials to use quarantine and then identifies the legislative gaps that SARS exposed in these laws. The article then looks at the current legislative reform efforts to create a more prepared legal environment in the event of another public health crisis such as SARS. In addition, the impact of quarantine on an individual and his or her family, including social and economic impacts, as well as its effect on the health care system is discussed. Finally, the legal limits on the use of quarantine are further examined. The author concludes that, because it is likely that a novel infectious agent such as SARS will surface in the future, the public health authorities must be vigilant by ensuring public health legal preparedness.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.635
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0000.000

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 head0.126
GPT teacher head0.443
Teacher spread0.317 · 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.

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

Citations4
Published2005
Admission routes2
Has abstractyes

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