National public health institutes in the Eastern Mediterranean Region: Insights from experts in the field
Bibliographic record
Abstract
National public health institutes (NPHIs) are crucial to the effectiveness of public health systems, including delivering essential public health functions and generating evidence for national health policies, strategies and plans. Currently, there is a significant lack of information regarding NPHI or NPHI-like organisations in Eastern Mediterranean Region (EMR) countries, including how they fit into their broader health systems governance landscape. NPHIs exist in 12 out of 22 EMR countries, yet there is no official International Association of National Public Health Institutes (IANPHI) regional network for the EMR, despite established IANPHI networks in four other regions. In 2022, the WHO's Eastern Mediterranean Regional Office led a study comprising an online survey and key informant interviews, which synthesised expert insights and summarised recommendations to strengthen the health systems governance-related role of NPHIs in EMR countries. Study participants included current and former high-level representatives of NPHIs, the government (eg, Ministries of Health, health regulatory authorities), multilateral organisations or non-governmental organisations focusing on health, and others identified as senior health systems governance experts from EMR. Insights and recommendations from experts varied widely, but there were also many common elements and overlaps. These included the need for enhancing NPHI functionalities and collaborative efforts with the public health sector (eg, Ministry of Health, Health Council) in health policy and decision-making formulation and implementation. This, in turn, requires advancing NPHI's fit-for-purpose and sustainable governance and financing arrangements, improving the accessibility and transparency of health data for NPHIs, strengthening engagement and collaboration between NPHIs and other health system actors (including the private sector), and promoting a more prominent role for NPHIs in the development and implementation of public health-related policies and legislation. While many excellent insights and thoughtful strategic guidance are provided, further adaptation may be needed to implement the proposed recommendations in different EMR country contexts going forward.
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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 | 0.045 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".