Launching the Global Health Network Middle East and North Africa Regional Network: A Path to Promote the Region’s Global Health Research Presence and Build Unity and Collaboration Towards Tackling Regional Public Health Priorities
Bibliographic record
Abstract
The Global Health Network Middle East and North Africa (TGHN MENA) was officially launched on 21 October 2024, representing a pivotal initiative to address the region's distinct and complex public health challenges. Building on the comprehensive global framework of the central TGHN network, the regional TGHN MENA network was founded by region-based experts with support from the TGHN team. The network was established as a pioneering initiative to bring together 18 partners from 14 countries, representing various sectors such as academia, policymakers, and governmental and non-governmental organizations, to tackle pressing issues such as chronic diseases, mental health, and climate change impacts. High-level panel discussions were held to define the goals of TGHN MENA in building resilient public health systems. This perspective outlines the network's vision for building resilient health systems through research prioritization and capacity strengthening, amidst growing uncertainties in the regional public health landscape. The MENA region has diverse and complex public health challenges related to health systems, emergencies, chronic disease, mental health disorders, and climate change, due to cultural, social, and geographic differences. The TGHN MENA network is a community of practice and can identify commonalities and priorities and find shareable solutions. Key strategies proposed include establishing an open-access, online platform to support knowledge exchange, implementing on-the-job training and capacity-strengthening initiatives, and emphasizing the use of artificial intelligence in public health research. This perspective outlines TGHN MENA's inaugural one-year action plan, which emphasizes regular knowledge-sharing activities, capacity-building initiatives, and sustained partners' commitment as foundational steps towards improved public health outcomes in the region.
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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 | 0.023 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".