A framework to support the integration of priority setting in the preparedness, alert, control and evaluation stages of a disease pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The COVID-19 pandemic, where the need-resource gap has necessitated decision makers in some contexts to ration access to life-saving interventions, has demonstrated the critical need for systematic and fair priority setting and resource allocation mechanisms. Disease outbreaks are becoming increasingly common and priority setting lessons from previous disease outbreaks could be better harnessed to inform decision making and planning for future disease outbreaks. The purpose of this paper is to discuss how priority setting and resource allocation could, ideally, be integrated into the WHO pandemic planning and preparedness framework and used to inform the COVID-19 pandemic recovery plans and plans for future outbreaks. Priority setting and resource allocation during disease outbreaks tend to evoke a process similar to the 'rule of rescue'. This results in inefficient and unfair resource allocation, negative effects on health and non-health programs and increased health inequities. Integrating priority setting and resource allocation activities throughout the four phases of the WHO emergency preparedness framework could ensure that priority setting during health emergencies is systematic, evidence informed and fair.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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