A survey of International Health Regulations National Focal Points experiences in carrying out their functions
Bibliographic record
Abstract
BACKGROUND: The 2005 International Health Regulations (IHR (2005)) require States Parties to establish National Focal Points (NFPs) responsible for notifying the World Health Organization (WHO) of potential events that might constitute public health emergencies of international concern (PHEICs), such as outbreaks of novel infectious diseases. Given the critical role of NFPs in the global surveillance and response system supported by the IHR, we sought to assess their experiences in carrying out their functions. METHODS: In collaboration with WHO officials, we administered a voluntary online survey to all 196 States Parties to the IHR (2005) in Africa, Asia, Europe, and South and North America, from October to November 2019. The survey was available in six languages via a secure internet-based system. RESULTS: In total, 121 NFP representatives answered the 56-question survey; 105 in full, and an additional 16 in part, resulting in a response rate of 62% (121 responses to 196 invitations to participate). The majority of NFPs knew how to notify the WHO of a potential PHEIC, and believed they have the content expertise to carry out their functions. Respondents found training workshops organized by WHO Regional Offices helpful on how to report PHEICs. NFPs experienced challenges in four critical areas: 1) insufficient intersectoral collaboration within their countries, including limited access to, or a lack of cooperation from, key relevant ministries; 2) inadequate communications, such as deficient information technology systems in place to carry out their functions in a timely fashion; 3) lack of authority to report potential PHEICs; and 4) inadequacies in some resources made available by the WHO, including a key tool - the NFP Guide. Finally, many NFP representatives expressed concern about how WHO uses the information they receive from NFPs. CONCLUSION: Our study, conducted just prior to the COVID-19 pandemic, illustrates key challenges experienced by NFPs that can affect States Parties and WHO performance when outbreaks occur. In order for NFPs to be able to rapidly and successfully communicate potential PHEICs such as COVID-19 in the future, continued measures need to be taken by both WHO and States Parties to ensure NFPs have the necessary authority, capacity, training, and resources to effectively carry out their functions as described in the IHR.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".