98:oral How did European countries set priorities in response to the COVID-19 threat? A comparative document analysis of 24 pandemic preparedness plans
Bibliographic record
Abstract
Introduction The COVID-19 pandemic has forced governments across Europe to consider how to prioritise the allocation of scarce resources. Many took decisions to increase funding for health services, and to redirect current fiscal, human and technical resource towards meeting the new threat. Methods We conducted document analysis of pandemic preparedness plans in 24 countries across the regions of Europe, focussing on prioritisation and allocation of health-related resources. To be included, countries needed to have publicly available COVID19 preparedness plans. Where necessary, plans were translated into English before two members of the team conducted data extraction. We adapted the Kapiriri and Martin (2010) framework as our organising data extraction tool. Following validity checks, these data were synthesised numerically and thematically. Results COVID19 has engendered recognition on behalf of government of the scarcity of health care resources. However, many plans still fell short of identifying specific budgetary implications or trade-offs between COVID19 responses and other service priorities. Many plans describe use of evidence, expert involvement and decision making criteria. However, use of formal priority setting tools and frameworks was rare. The plans included very little engagement with citizens and service users, and equity considerations were often under-developed. The overall average compliance with quality parameters of priority setting was 29%. Discussion The plans indicate a political commitment to priority setting but underline the relative failure of priority setting methodologies to become embedded in governmental decision making processes. In the balance between ‘technocratic’ elements of priority setting and ‘processual’ dimensions, there was an emphasis on the former, reflecting the enforced speed with which plans were drawn up. As difficult priority setting decisions will be required in the post-crisis phase (as care backlogs and unmet need are addressed) it is likely that a rebalancing towards the processual aspects of decision making processes will be required.
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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 | 0.032 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".