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Record W2124301066 · doi:10.12927/hcpol.2007.18526

Pandemic Threats and the Need for New Emergency Public Health Legislation in Canada

2006· article· en· W2124301066 on OpenAlexafffundvenueabout
Kumanan Wilson

Bibliographic record

VenueHealthcare policy · 2006
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsLegislationLegislaturePublic healthGovernment (linguistics)Public administrationAgency (philosophy)Emergency managementBusinessPolitical sciencePublic relationsMedicineLawSociologyNursing

Abstract

fetched live from OpenAlex

The 2003 outbreak of Severe Acute Respiratory Syndrome (SARS) exposed serious limitations in Canada's ability to respond to a public health emergency. Considerable progress has been made since SARS in addressing these limitations, including the creation of the new Public Health Agency of Canada. A remaining contentious question is whether there is a need for new federal emergency public health powers. Approaches to public health problems are best handled through collaborative processes, recognizing the critical importance of the local public health response. Nevertheless, this paper argues that a legislative back-up plan must be available to the federal government in the event that collaborative relationships break down. At the minimum, legislation should give the federal government the authority to have guaranteed access to surveillance data during a public health emergency. The legislation should also consider providing the federal government with the authority to devote the nation's resources to the management of an emergency at its earliest stages. However, any legislative approach must be combined with the development of appropriate capacity at the national level to ensure that new powers can be adequately utilized and that required funding reaches public health officials at other levels of government.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.847
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.154
GPT teacher head0.478
Teacher spread0.324 · 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 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

Citations10
Published2006
Admission routes4
Has abstractyes

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