Ethics of resource allocation in a public health emergency context
Bibliographic record
Abstract
Resource allocation under non-emergency conditions is often challenging. Within the context of a Public Health Emergency (PHE), allocation decisions become significantly more difficult as decisions are often necessary on very short timelines, where relevant information (either evidence or information "on the ground") is changing or incomplete, there is significant potential for harm, and resources are scarce, in unpredictable supply, and likely in high demand. An intentional value-based decision-making approach in such circumstances can clarify the values that ought to guide decisions, offering transparency and consistency, among other benefits. We use the example of vaccine allocation during the COVID-19 pandemic to explore value-based decision-making within a PHE context. We describe several core values that are relevant to PHE decision-making and outline their implications for approaches to vaccine allocation. While we focus on vaccine allocation, the values discussed are relevant to other system-level decisions in both emergency and non-emergency situations. Tips for leaders wishing to adopt a value-based approach to decision-making are offered.
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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 | 0.037 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".