Learning From COVID-19: What Would It Take to Be Better Prepared in the Eastern Mediterranean Region?
Bibliographic record
Abstract
The COVID-19 transmission in the Eastern Mediterranean Region (EMR) was influenced by various factors such as conflict, demographics, travel and social restrictions, migrant workers, weak health systems, and mass gatherings. The countries that responded well to COVID-19 had high-level political commitment, multisectoral coordination, and existing infrastructures that could quickly mobilize. However, some EMR countries faced challenges due to political instability and fragile health systems, which hindered their response strategies. The pandemic highlighted the region's weak health systems and preparedness, fragmented surveillance systems, and lack of trust in information sharing. COVID-19 exposed the disruption of access and delivery of essential health services as a major health system fragility. In 2020, the World Health Organization (WHO) conducted a global pulse survey, which demonstrated that the EMR experienced the highest disruption in health services compared to other WHO regions. However, thanks to prioritization by the WHO and its member states, significant improvement was observed in 2021 during the second round of the WHO's National Pulse Survey. The pandemic underscored the importance of political leadership, community engagement, and trust and emphasized that investing in health security benefits everyone. Increasing vaccine coverage, building regional capacities, strengthening health systems, and working toward universal health coverage and health security are all priorities in the EMR. Emergency public health plays a key role in preparing for and responding to pandemics and biological threats. Integrating public health into primary care and investing in public health workforce capacity building is essential to reshaping public health and health emergency preparedness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".