Deference or deliberation: rethinking the judicial role in the allocation of healthcare resources.
Bibliographic record
Abstract
The development of strategies by which healthcare resources are explicitly rationed has created significant challenges for many governments. In particular, those undertaking allocative decisions may struggle to establish sufficient legitimacy to enable them to make choices which are morally and politically controversial without generating distrust and resistance, which could jeopardise the effectiveness of the decision-making regime. This article considers possible means of addressing this difficulty from the perspective of public law. The mechanism which is currently favoured, most clearly seen in the UK, is to establish regulatory agencies which apply scientific and social-scientific methodologies to priority-setting questions. This has not been entirely successful. Accordingly, the article will propose a more developed role for courts, which can require that reasoned, relevant justifications for allocative choices are offered and thus provide a foundation for broad public deliberation on rationing. However, in order to fulfil such a function, the judiciary will need to modify its traditionally deferential stance on issues of this type. South African and Canadian cases illustrate how such a change may come about.
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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 | 0.087 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.067 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".