Why we should not ‘just use age’ for COVID-19 vaccine prioritisation
Bibliographic record
Abstract
Older age is one of the greatest risk factors for severe outcomes from COVID-19. If we believe it is important to use limited supplies of COVID-19 vaccines to protect the most vulnerable and prevent deaths, then available doses should be allocated with significant priority to older adults. Yet, we should resist the conclusion that age should be the sole criterion for COVID-19 vaccine prioritisation or that no younger populations (eg, those under the age of 60) should be prioritised until all older adults have been vaccinated. This article examines arguments that are commonly presented to abandon 'complex' vaccine prioritisation schemes in favour of 'just using age' (eg, prioritising those 80 years of age and older and then decreasing in a 5-year age bands until the entire population has had the opportunity to be vaccinated), and articulates the ethical reasons why these arguments are not persuasive.
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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 | 0.097 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.022 | 0.033 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".