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Record W2116248392 · doi:10.1080/13648470701381432

Which Child Will Live or Die in France: Examining Physician Responsibility for Critically Ill Children

2007· article· en· W2116248392 on OpenAlexfundno aff
Franco A. Carnevale, Gilles Bibeau

Bibliographic record

VenueAnthropology and Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsnot available
FundersAssociated Medical Services
KeywordsAgency (philosophy)Critically illContext (archaeology)Best interestsMoral responsibilityWelfareMoral agencyPsychologyNursingMedicineSociologySocial psychologyPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

There exists some controversy regarding the roles of parents and physicians for life-support decisions for critically ill children. French medical literature asserts that it is inappropriate for parents to bear such responsibilities. Physicians are commonly responsible for these decisions. The aims of this study were to (a) outline how life-support decisions are made for critically ill children in France; (b) examine the cultural context within which these decisional practices have arisen; and (c) analyse the ethical implications of these practices. Data were obtained in 2004 from consultations with relevant experts, relevant French medical guidelines, French print media, empirical research reports and related seminal publications. Specific themes that were identified included: (1) the physician is responsible for medical decision making for children; (2) French physicians caring for critically ill children bear a societal responsibility for preventing severely 'handicapped' survivors; (3) physician authority is rooted in State responsibility for children's welfare; and (4) active euthanasia is sometimes practised to prevent the creation of 'les handicapés'. These findings highlight ethical concerns that can result from assigning some physicians such largely unquestioned societal moral agency.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.419
Teacher spread0.384 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2007
Admission routes1
Has abstractyes

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