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Record W4399296251 · doi:10.1017/9781009570060

Capacity, Informed Consent and Third-Party Decision-Making

2024· book· en· W4399296251 on OpenAlexaboutno aff
Jacob M. Appel

Bibliographic record

VenueCambridge University Press eBooks · 2024
Typebook
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsnot available
Fundersnot available
KeywordsInformed consentThird partyPsychologyInternet privacyComputer scienceMedicineAlternative medicine

Abstract

fetched live from OpenAlex

This Element examines three related topics in the field of bioethics that arise frequently both in clinical care and in medico-legal settings: capacity, informed consent, and third-party decision-making. All three of these subjects have been shaped significantly by the shift from the paternalistic models of care that dominated medicine in the United States, Canada, and Great Britain prior to the 1960s to the present models that privilege patient autonomy. Each section traces the history of one of these topics and then explores the major ethics issues that arise as these issues are addressed in contemporary clinical practice, paying particular attention to the role that structural factors such as bias and social capital play in their use. In addition, the volume also discusses recent innovations and proposals for reform that may shape these subjects in the future in response both to technological advances and changes in societal priorities.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.320
Teacher spread0.262 · 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 teacher head, not a consensus.

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

Citations4
Published2024
Admission routes1
Has abstractyes

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