Providing care for the 99.9% during the COVID-19 pandemic: How ethics, equity, epidemiology, and cost per QALY inform healthcare policy
Bibliographic record
Abstract
Managing healthcare in the Coronavirus Disease 2019 (COVID-19) era should be guided by ethics, epidemiology, equity, and economics, not emotion. Ethical healthcare policies ensure equitable access to care for patients regardless of whether they have COVID-19 or another disease. Because healthcare resources are limited, a cost per Quality Life Year (QALY) approach to COVID-19 policy should also be considered. Policies that focus solely on mitigating COVID-19 are likely to be ethically or financially unsustainable. A cost/QALY approach could target resources to optimally improve QALYs. For example, most COVID-19 deaths occur in long-term care facilities, and this problem is likely better addressed by a focused long-term care reform than by a society-wide non-pharmacological intervention. Likewise, ramping up elective, non-COVID-19 care in low prevalence regions while expanding testing and case tracking in hot spots could reduce excess mortality from non-COVID-19 diseases and decrease adverse financial impacts while controlling the epidemic. Globally, only ∼0.1% of people have had a COVID-19 infection. Thus, ethical healthcare policy must address the needs of the 99.9%.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; a candidate call from one teacher head, 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".