COVD-08. THE DIVIDED PRINCIPLE OF JUSTICE: ETHICAL DECISION-MAKING IN CANCER CARE DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Abstract The four-principle approach to medical ethics, balancing prima facie obligations to beneficence, nonmaleficence, autonomy, and justice, has supplied a common language for the application of ethical analysis to medical practice for the last four decades. The frayed edges of this edifice are made visible, however, by the ongoing COVID-19 pandemic (and other historical circumstances of severe resource limitation in the healthcare system). We interrogate ethical considerations involved in the state of medical care during the COVID-19 pandemic, as demonstrated by reconsiderations of cancer care, in which the pillar of justice is exposed as internally divided. Specifically, we identify both patient-oriented and system-oriented principles of justice constituting a broader collective, unique among the classical four principles. This leads us to suggest a formal recognition of justice as a divided category, and a reclassification of the term into two subcategories which serve fundamentally different interests. The result is a more cohesive four principle approach in which all principles favour the deontological relationships fostered between patients and providers, which exists in constant balance with the utilitarian interests of the broader medical system.
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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 | 0.034 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.055 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 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".