110:oral Profile of priority setting incorporated to COVID-19 response plans in 86 countries in the world
Bibliographic record
Abstract
Background The COVID-19 pandemic has imposed a burden on all health systems budgets and pushed policymakers to rapidly set priorities for resource allocation. This study aimed to identify quality parameters of priority setting (PS) incorporated in a sample of the national response plans. Methods We reviewed a sample of COVID-19 national response plans from 86 countries across six regions of the WHO to assess the degree to which they included twenty quality indicators of effective PS. A quantitative descriptive analysis was used to explore the profile of PS according to independent variables. Results The countries sampled represent 40% of countries 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 PS quality indicators emerged by WHO region or country income levels. As per the quality PS parameters, evidence of political will, stakeholder participation, use of scientific evidence/adoption of WHO recommendations were each found in over 80% of plans. Regarding the frequency of other parameters we found, description of a specific PS process (7%); explicit criteria for PS (36,5%); inclusion of publicity strategies (65%), mention of mechanisms for enforcing decisions, either for appealing decisions or implementing strategies to improve internal accountability and reduce corruption (20%); explicit reference to public values (15%); description of means for enhancing compliance with the decisions (5%). Conclusion We found some emphasis on PS according to contextual factors. For instance, LMICs receiving international donations presented more detailed descriptions of resources required, plans for allocating resources and improving internal accountability. HICs more likely described stakeholder participation, mechanisms for public communication, and explicit PS processes. However, no country included all twenty parameters of PS.
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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 | 0.018 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".