Ethical Challenges Arising in the COVID-19 Pandemic: An Overview from the Association of Bioethics Program Directors (ABPD) Task Force
Bibliographic record
Abstract
The COVID-19 pandemic has raised a host of ethical challenges, but key among these has been the possibility that health care systems might need to ration scarce critical care resources. Rationing policies for pandemics differ by institution, health system, and applicable law. Most seem to agree that a patient's ability to benefit from treatment and to survive are first-order considerations. However, there is debate about what clinical measures should be used to make that determination and about other factors that might be ethically appropriate to consider. In this paper, we discuss resource allocation and several related ethical challenges to the healthcare system and society, including how to define benefit, how to handle informed consent, the special needs of pediatric patients, how to engage communities in these difficult decisions, and how to mitigate concerns of discrimination and the effects of structural inequities.
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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 | 0.042 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.012 | 0.023 |
| Insufficient payload (model declined to judge) | 0.002 | 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".