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Record W4313205914 · doi:10.1016/j.glohj.2022.12.002

A public health mission in Canada in response to the coronavirus disease 2019 (COVID-19) pandemic

2022· article· en· W4313205914 on OpenAlexaffabout
Marie Lavoie

Bibliographic record

VenueGlobal Health Journal · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsYork University
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Public healthBusinessStimulus (psychology)Work (physics)Political scienceDiseaseEconomic growthPublic relationsInfectious disease (medical specialty)EconomicsMedicineEngineering

Abstract

fetched live from OpenAlex

Many governments in the world reacted to the coronavirus disease 2019 pandemic by swiftly offering stimulus packages to their populations. While public unpreparedness was dramatic, it was not unexpected: many alarms had been sounded. Strategies by the federal and various provincial governments of Canada in core sectors predisposed the country to the current situation and weakened its capacity to respond adequately. This paper reviews the cumulative effects of these strategic orientations: a deficient investment strategy in fundamental science; disconnect between laboratory work and the country's capacity to produce vaccines and antiviral drugs; the priority of cost efficiency that led to overwhelming dependency on foreign production of medical supplies; and dramatic spending cuts in public health. We will discuss a Mission strategy to exit the crisis that takes a long-term perspective, in which public interest and public health, combined with a strong State leadership, stimulate innovation and collaboration between national and international actors.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.972

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.004
Scholarly communication0.0070.001
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.587
GPT teacher head0.512
Teacher spread0.074 · 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
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

Citations3
Published2022
Admission routes2
Has abstractyes

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