National focal points and implementation of the International Health Regulations
Bibliographic record
Abstract
As the COVID-19 pandemic continues, the World Health Organization (WHO), the International Health Regulations (IHR) and countries’ adherence to IHR guidance are coming under scrutiny and review. The IHR constitute a legal and governance framework that guides countries in responding to serious disease events while avoiding unnecessary interference with international trade and traffic. The IHR require States Parties to designate or establish national IHR focal points to facilitate information sharing about disease events with WHO, which makes these focal points critical in the effective implementation of the IHR within and between countries. On behalf of the State Party concerned, national focal points are responsible for timely notification to WHO of relevant health events, responding to WHO Secretariat requests for event-related information, and ensuring that messages and advice from WHO are disseminated to the relevant sectors within the country. A review of the 2013–2016 Ebola virus disease outbreak in West Africa found deficiencies in the functioning of national focal points.4 Published studies have also identified technical and political challenges to the notification of events by focal points to WHO. At the request of WHO, we evaluated the ability of focal points to carry out their IHR functions through 25 in-depth interviews and 105 online quantitative surveys. Here we present summary findings and recommendations emerging from our study; survey methods and results have been previously published.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".