Healthcare Decision-Making and the Law: Autonomy, Capacity and the Limits of Liberalism
Bibliographic record
Abstract
This analysis of the law's approach to healthcare decision-making critiques its liberal foundations in respect of three categories of people: adults with capacity, adults without capacity and adults who are subject to mental health legislation. Focusing primarily on the law in England and Wales, the analysis also draws on the law in the United States, legal positions in Australia, Canada, Ireland, New Zealand and Scotland and on the human rights protections provided by the ECHR and the Convention on the Rights of Persons with Disabilities. Having identified the limitations of a legal view of autonomy as primarily a principle of non-interference, Mary Donnelly questions the effectiveness of capacity as a gatekeeper for the right of autonomy and advocates both an increased role for human rights in developing the conceptual basis for the law and the grounding of future legal developments in a close empirical interrogation of the law in practice
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".