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Record W2026423454 · doi:10.2471/blt.14.137828

Achieving compliance with the International Health Regulations by overseas territories of the United Kingdom of Great Britain and Northern Ireland

2014· article· en· W2026423454 on OpenAlexfundno aff
Esther Hamblion, Mark Salter, Jane Jones

Bibliographic record

VenueBulletin of the World Health Organization · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPublic Health Policies and Education
Canadian institutionsnot available
FundersMcGill University
KeywordsPublic healthMember statesChinaPopulationInternational Health RegulationsPolitical scienceCompliance (psychology)Economic growthEnvironmental protectionGeographyEnvironmental healthMedicineEuropean unionBusinessCoronavirus disease 2019 (COVID-19)LawInternational tradeDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The 2005 International Health Regulations (IHR) came into force for all Member States of the World Health Organization (WHO) in June 2007 and the deadline for achieving compliance was June 2012. The purpose of the IHR is to prevent, protect against, control - and provide a public health response to - international spread of disease. The territory of the United Kingdom of Great Britain and Northern Ireland and that of several other Member States, such as China, Denmark, France, the Netherlands and the United States of America, include overseas territories, which cover a total population of approximately 15 million people. Member States have a responsibility to ensure that all parts of their territory comply with the IHR. Since WHO has not provided specific guidance on compliance in the special circumstances of the overseas territories of Member States, compliance by these territories is an issue for self-assessment by Member States themselves. To date, no reports have been published on the assessment of IHR compliance in countries with overseas territories. We describe a gap analysis done in the United Kingdom to assess IHR compliance of its overseas territories. The findings and conclusions are broadly applicable to other countries with overseas territories which may have yet to assess their compliance with the IHR. Such assessments are needed to ensure compliance across all parts of a Member States' territory and to increase 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)0.001
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 score0.619
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.031
GPT teacher head0.348
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 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

Citations4
Published2014
Admission routes1
Has abstractyes

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