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Record W4392970927 · doi:10.1017/s0963180124000148

Refusals and Requests: In Defense of Consistency

2024· article· en· W4392970927 on OpenAlexaff
J. Ronnie Davis, Eric Mathison

Bibliographic record

VenueCambridge Quarterly of Healthcare Ethics · 2024
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersUniversity of Cambridge
KeywordsHarmDutyAppealObligationAutonomyConsistency (knowledge bases)PsychologyCoercion (linguistics)PreferenceLaw and economicsLawHealth careSocial psychologyPolitical scienceMedicineSociologyPhilosophyComputer scienceEconomics

Abstract

fetched live from OpenAlex

Physicians place significant weight on the distinction between acts and omissions. Most believe that autonomous refusals for procedures, such as blood transfusions and resuscitation, ought to be respected, but they feel no similar obligation to accede to requests for treatment that will, in the physician's opinion, harm the patient (e.g., assisted death). Thus, there is an asymmetry. In this paper, we challenge the strength of this distinction by arguing that the ordering of values should be the same in both cases. The reason for respecting refusals is that, in such cases, autonomy outweighs well-being. We argue that the same should be true in request cases, which means that requests should not be denied only due to the treatment being too harmful in the physician's opinion. Our strategy is to consider and reject a number of arguments for the asymmetrical view, including an appeal to the doing-allowing distinction and positive and negative rights. The duty to respect refusals is still greater than the duty to grant requests on our view, but, by arguing that the ordering of values is the same in both cases, we show that there is less of a distinction in healthcare between requests and refusals than many currently believe.

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)0.019
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.012
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 head0.114
GPT teacher head0.501
Teacher spread0.387 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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