How did European countries set health priorities in response to the COVID-19 threat? A comparative document analysis of 24 pandemic preparedness plans across the EURO region
Bibliographic record
Abstract
The COVID-19 pandemic has forced governments across the world to consider how to prioritise the allocation of scarce resources. There are many tools and frameworks that have been designed to assist with the challenges of priority setting in health care. The purpose of this study was to examine the extent to which formal priority setting was evident in the pandemic plans produced by countries in the World Health Organisation's EURO region, during the first wave of the COVID-19 pandemic. This compliments analysis of similar plans produced in other regions of the world. Twenty four pandemic preparedness plans were obtained that had been published between March and September 2020. For data extraction, we applied a framework for identifying and assessing the elements of good priority setting to each plan, before conducting comparative analysis across the sample. Our findings suggest that while some pre-requisites for effective priority setting were present in many cases - including political commitment and a recognition of the need for allocation decisions - many other hallmarks were less evident, such as explicit ethical criteria, decision making frameworks, and engagement processes. This study provides a unique insight into the role of priority setting in the European response to the onset of the COVID-19 pandemic.
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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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".