The Eastern Mediterranean Public Health Network: A Resource for Improving Public Health in the Eastern Mediterranean Region
Bibliographic record
Abstract
Countries in the Eastern Mediterranean Region (EMR) face many challenges in terms of improving population health and progressing toward sustainable development goals (SDGs). This paper aims to describe the approach taken by the Eastern Mediterranean Public Health Network (EMPHNET) to help strengthen health systems in the EMR and enable progress toward sustainable development targets, the tools it used, and its achievements. The EMPHNET is a nonprofit organization that has worked to support EMR countries in strengthening their public health systems since its establishment in 2009. The EMPHNET invests in building workforce capacity in applied epidemiology by supporting field epidemiology training programs in more than 10 countries in the EMR, while ensuring country ownership of these programs. The EMPHNET established the Global Health Development (GHD) to maximize support for positive change and SDG progress. As an implementing arm to the EMPHNET, GHD aligns its strategies with national policies and directions. The GHD/EMPHNET works at the regional, national, and subnational levels and tailors solutions for the local context. Over the past years, the EMPHNET succeeded in partnering with over 13 countries and provided technical assistance to leverage country efforts and maximize resource use. The EMPHNET's Center of Excellence for Applied Epidemiology focuses on building capacity in population health and applied epidemiology. The EMPHNET supports countries in delivering effective public health programs by building capacity and conducting research to prevent and control emerging and reemerging diseases, vaccine-preventable diseases, and noncommunicable diseases. The commitment to the region, together with the increased trust and assertion from the countries, helped GHD/EMPHNET build a strong portfolio, which was made possible by the interconnected effort that continues to nurture and foster better health among people living in the EMR.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".