Priority setting in times of crises: an analysis of priority setting for the COVID-19 response in the Western Pacific Region
Bibliographic record
Abstract
BACKGROUND: While priority setting is recognized as critical for promoting accountability and transparency in health system planning, its role in supporting rational, equitable and fair pandemic planning and responses is less well understood. This study aims to describe how priority setting was used to support planning in the initial stage of the pandemic response in a subset of countries in the Western Pacific Region (WPR). METHODS: We purposively sampled a subset of countries from WPR and undertook a critical document review of the initial national COVID-19 pandemic response plans. A pre-specified tool guided data extraction and the analysis examined the use of quality parameters of priority setting, and equity considerations. RESULTS: Nine plans were included in this analysis, from the following countries: Papua New Guinea, Tonga, The Philippines, Fiji, China, Australia, New Zealand, Japan, and Taiwan. Most commonly the plans described strong political will to respond swiftly, resource needs, stakeholder engagement, and defined the roles of institutions that guided COVID-19 response decision-making. The initial plans did not reflect strong evidence of public engagement or considerations of equity informing the early responses to the pandemic. CONCLUSION: This study advances an understanding of how priority setting and equity considerations were integrated to support the development of the initial COVID-19 responses in nine countries in WPR and contributes to the literature on health system planning during emergencies. This baseline assessment reveals evidence of the common priority setting parameters that were deployed in the initial responses, the prioritized resources and equity considerations and reinforces the importance of strengthening health system capacity for priority setting to support future pandemic preparedness.
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How this classification was reachedexpand
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 | 0.069 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".