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Record W4205472317 · doi:10.1017/cbo9780511760679

Healthcare Decision-Making and the Law: Autonomy, Capacity and the Limits of Liberalism

2010· book· en· W4205472317 on OpenAlexaboutno aff
Mary Donnelly

Bibliographic record

Venuenot available
Typebook
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsnot available
Fundersnot available
KeywordsAutonomyLawHuman rightsLegislationPolitical scienceHealth careInternational human rights lawConventionConvention on the Rights of Persons with DisabilitiesSubject (documents)Sociology

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.120
Scholarly communication0.0150.011
Open science0.0020.007
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.042
GPT teacher head0.356
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations50
Published2010
Admission routes1
Has abstractyes

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