Priority setting for pandemic preparedness and response: A comparative analysis of COVID-19 pandemic plans in 12 countries in the Eastern Mediterranean Region
Bibliographic record
Abstract
Background: The COVID-19 pandemic has significantly disrupted health systems and exacerbated pre-existing resource gaps in the Eastern Mediterranean Region (WHO-EMRO). Active humanitarian and refugee crises have led to mass population displacement and increased health system fragility, which has implication for equitable priority setting (PS). We examine whether and how PS was included in national COVID-19 pandemic plans within EMRO. Methods: An analysis of COVID-19 pandemic response and preparedness planning documents from a sample of 12/22 countries in WHO-EMRO. We assessed the degree to which documented PS processes adhere to twenty established quality parameters of effective PS. Results: While all reviewed plans addressed some aspect of PS, none included all quality parameters. Yemen's plan included the highest number (9) of quality parameters, while Egypt's addressed the lowest (3). Most plans used evidence in their planning processes. While no plans explicitly identify equity as a criterion to guide PS; many identified vulnerable populations - a key component of equitable PS. Despite high concentrations of refugees, migrants, and IDPs in EMRO, only a quarter of the plans identified them as vulnerable. Conclusion: PS setting challenges are exacerbated by conflict and the resulting health system fragmentation. Systematic and quality PS is essential to tackle long-term health implications of COVID-19 for vulnerable populations in this region, and to support effective PS and equitable resource allocation.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".