110:oral Profile of priority setting incorporated to COVID-19 response plans in 86 countries in the world
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Bibliographic record
Abstract
<h3>Background</h3> 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. <h3>Methods</h3> 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. <h3>Results</h3> 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%). <h3>Conclusion</h3> 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it