COVID‐19, rationing and the right to health: can patients bring legal actions if they are denied access to care?
Bibliographic record
Abstract
As health providers worldwide respond to the COVID-19 pandemic, clinicians and communities are asking questions about who will be given access to scarce healthcare resources. While resource allocation decision-making frameworks have been developed, or adapted, to govern the unprecedented impacts of the pandemic, access to healthcare may, now and in the future, be restricted in ways previously unimaginable. While the broader community may accept the need for rationing, individuals denied COVID-19 or other care might look to the law to protect their interests. Clinicians may have questions about any legal consequences of denying patients access to health services. This article explores the nature of health rights in Australia and the difficulties in bringing legal cases that challenge resource allocation decisions during a pandemic or its aftermath.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.046 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 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".