Rethinking the World Health Organization’s leadership of global health governance and the global health surveillance systems
Bibliographic record
Abstract
Global health governance is a strategic priority for the World Health Organization (WHO), and the public health surveillance system (PHSS) is a fundamental element of the global health governance structure to timely identify emerging diseases and guide global public health decisions and actions. This analysis explores the overall landscape of global health governance, with a specific focus on the PHSS to understand whether the existing governance landscape facilitates or undermines the WHO's ability to formulate and implement global health policies and initiatives. To achieve this, the existing evidence was reviewed, and synthesized with the experts' perspectives. It is reported that fragmentation is the main drawback of the global health governance landscape, necessitating reorganization and restructuring. The disintegration of PHSS at the global, regional and local levels is associated with a lack of leadership, misalignment with global health priorities, imbalance in coverage of surveillance systems, inadequate innovative technology and digitalization, and fragmented data and information systems. The fragmentation and disintegration of global health governance undermine the effectiveness of the WHO's global health strategic directions and programmes and hinder its ability to govern and guide the global, regional and national public health emergency response. Strategic rethinking of the WHO's governance is essential because strong governance and leadership lead to a robust, aligned and effective PHSS.
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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 | 0.078 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".