165:oral The role of priority setting in 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 in the Eastern Mediterranean Region (WHO-EMRO), where over half of the countries are affected by armed conflict. Active humanitarian and refugee crises have led to mass population displacement and increased health system fragility. This has exacerbated pre-existing resource gaps and increased competition for meager resources. With large proportions of vulnerable populations - refugees, migrants, and internally displaced people (IDPs) - their explicit consideration in planning documents is critical if equitable priority setting is to be realized during the pandemic. We examine what and how priority setting (PS) was included in national COVID-19 pandemic plans within the region. Methods An analysis of COVID-19 pandemic response and preparedness planning documents from a sample of twelve purposively selected countries in WHO-EMRO. We assessed the degree to which documented PS processes adhere to twenty established quality indicators of effective PS from Kapiriri & Martin’s framework. Results While all reviewed plans addressed some aspect of PS, none included all quality parameters. Yemen’s plan included the most quality parameters (12), while Egypt’s addressed the least (4). Publicity of priorities was common to all plans. The next mostly commonly identified parameter was use of evidence to guide planning and PS. When considering equity as a PS criterion, despite the high concentration of refugees, migrant, and IDPs in the region, only a quarter of the plans prioritized these populations. Discussion When setting priorities in health emergencies, context is paramount. In areas experiencing conflict and crisis, PS can be an undemocratic and challenging process. Health system fragmentation is key contributor to COVID-19 inequities experienced across the EMRO region. Limited prioritization of vulnerable groups like refugees, migrant, and IDPs in planning documents, will have long-term health implications and exacerbate the disproportionate burden of COVID illness and death for these groups.
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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".