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Record W2106447438 · doi:10.1093/heapol/czq026

Strengthening the International Health Regulations: lessons from the H1N1 pandemic

2010· article· en· W2106447438 on OpenAlexaff
Kumanan Wilson, John S. Brownstein, David P. Fidler

Bibliographic record

VenueHealth Policy and Planning · 2010
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of Ottawa
FundersU.S. National Library of MedicineNational Institutes of Health
KeywordsInternational Health RegulationsPublic healthPandemicGlobal healthBusinessInternational healthScope (computer science)Corporate governanceHealth policyEconomic growthPolitical scienceEnvironmental healthMedicineDiseaseCoronavirus disease 2019 (COVID-19)Infectious disease (medical specialty)Economics

Abstract

fetched live from OpenAlex

The International Health Regulations (2005) [IHR(2005)] represent a potentially revolutionary change in global health governance. The use of the regulations by the World Health Organization (WHO) to respond to the outbreak of pandemic influenza A 2009-H1N1 highlights the importance of the regulations to protecting global health security. As the 2009-H1N1 pandemic illustrated, the IHR(2005) have provided a more robust framework for responding to public health emergencies of international concern (PHEICs), through requiring reporting of serious disease events, strengthening how countries and WHO communicate concerning health threats, empowering the WHO Director-General to declare the existence of PHEICs and to issue temporary recommendations for responding to them, and requiring countries not to implement measures that unnecessarily restrict trade and travel or infringe on human rights. However, limitations to the effectiveness of the IHR(2005) revealed in the 2009-H1N1 pandemic include continuing inadequacies in surveillance and response capacities within some countries, violations of IHR(2005) rules and a potentially narrowing scope of application only to influenza-like pandemic events. These limitations could undermine the IHR(2005)'s potential to contribute to national and global efforts to detect and mitigate future public health emergencies. Support for the IHR(2005) should be broadened and deepened to improve their utility as a tool to strengthen global health security.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.494
Threshold uncertainty score0.990

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 integrity0.0000.001
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 head0.128
GPT teacher head0.485
Teacher spread0.357 · 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

Citations53
Published2010
Admission routes1
Has abstractyes

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