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 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 | 0.089 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.018 | 0.030 |
| Insufficient payload (model declined to judge) | 0.011 | 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".