Examining priority setting in the national COVID-19 pandemic plans: A case study from countries in the WHO- South-East Asia Region (WHO-SEARO)
Bibliographic record
Abstract
Background: The World Health Organization- South-East Asia Region (WHO-SEARO) accounted for almost 17% of all the confirmed cases and deaths of COVID-19 worldwide. While the literature has documented a weak COVID-19 response in the WHO-SEARO, there has been no discussion of the degree to which this could have been influenced/ mitigated with the integration of priority setting (PS) in the region's COVID-19 response. The purpose of this paper is to describe the degree to which the COVID-19 plans from a sample of WHO-SEARO countries included priority setting. Methods: The study was based on an analysis of national COVID-19 pandemic response and preparedness planning documents from a sample of seven (of the eleven) countries in WHO-SEARO. We described the degree to which the documented priority setting processes adhered to twenty established quality indicators of effective PS and conducted a cross-country comparison. Results: All of the reviewed plans described the required resources during the COVID-19 pandemic. Most, but not all of the plans demonstrated political will, and described stakeholder involvement. However, none of the plans presented a clear description of the PS process including a formal PS framework, and PS criteria. Overall, most of the plans included only a limited number of quality indicators for effective PS. Discussion and conclusion: There was wide variation in the parameters of effective PS in the reviewed plans. However, there were no systematic variations between the parameters presented in the plans and the country's economic, health system and pandemic and PS context and experiences. The political nature of the pandemic, and its high resource demands could have influenced the inclusion of the parameters that were apparent in all the plans. The finding that the plans did not include most of the evidence-based parameters of effective PS highlights the need for further research on how countries operationalize priority setting in their respective contexts as well as deeper understanding of the parameters that are deemed relevant. Further research should explore and describe the experiences of implementing defined priorities and the impact of this decision-making on the pandemic outcomes in each country.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".