Was priority setting considered in COVID-19 response planning? A global comparative analysis
Bibliographic record
Abstract
Background: The COVID-19 pandemic forced governments across the world to consider how to prioritize resource allocation. Most countries produced pandemic preparedness plans that guide and coordinate healthcare, including how to allocate scarce resources such as ventilators, human resources, and therapeutics. The objective of this study was to compare and contrast the extent to which established parameters for effective priority setting (PS) were incorporated into COVID-19 pandemic response planning in several countries around the world. Methods: We used the Kapriri and Martin framework for effective priority setting and performed a quantitative descriptive analysis to explore whether and how countries' type of health system, political, and economic contexts impacted the inclusion of those parameters in their COVID-19 pandemic plans. We analyzed 86 country plans across six regions of the World Health Organization. Results: The countries sampled represent 40% of nations in AFRO, 54.5% of EMRO, 45% of EURO, 46% of PAHO, 64% of SEARO, and 41% of WPRO. They also represent 39% of all HICs in the world, 39% of Upper-Middle, 54% of Lower-Middle, and 48% of LICs. No pattern in attention to parameters of PS emerged by WHO region or country income levels. The parameters: evidence of political will, stakeholder participation, and use of scientific evidence/ adoption of WHO recommendations were each found in over 80% of plans. We identified a description of a specific PS process in 7% of the plans; explicit criteria for PS in 36.5%; inclusion of publicity strategies in 65%; mention of mechanisms for appealing decisions or implementing procedures to improve internal accountability and reduce corruption in 20%; explicit reference to public values in 15%; and a description of means for enhancing compliance with the decisions in 5%. Conclusion: The findings provide a basis for policymakers to reflect on their prioritization plans and identify areas that need to be strengthened. Overall, there is little consideration for explicit prioritization processes and tools and restricted attention to equity considerations; this may be a starting point for policymakers interested in improving future preparedness and response planning. Although the study focused on the COVID-19 pandemic, priority setting remains one of the policymakers' most prominent challenges. Policymakers should consider integrating systematic priority setting in their routine decision-making processes.
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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 | 0.062 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".