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Record W2083357184 · doi:10.1353/nib.2013.0059

How Contextual and Relational Aspects Shape the Perspective of Healthcare Providers on Decision Making for Patients With Disorders of Consciousness: A Qualitative Interview Study

2013· article· en· W2083357184 on OpenAlexfundno aff
Catherine Rodrigue, Richard J. Riopelle, James L. Bernat, Éric Racine

Bibliographic record

VenueNarrative Inquiry in Bioethics · 2013
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsPerspective (graphical)Qualitative researchConsciousnessHealth carePsychologyPersistent vegetative stateClinical decision makingMedicineSociologyMinimally conscious stateFamily medicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Disorders of consciousness (DOC) are a family of related neurological syndromes characterized by deficits of varying degrees of wakefulness (e.g., sleep-wake cycles and arousal) or awareness (e.g., reacting to stimuli, interacting with the environment). Although coma rarely persists for more than a few weeks, some patients remain in a subsequent vegetative state or a minimally conscious state for months or years. Caring for patients with DOC raises ethical questions, but the perspectives of healthcare providers on these questions remain poorly documented. We conducted a qualitative study involving healthcare providers with different backgrounds. Semistructured interviews were used to explore attitudes toward ethical issues. We found that contextual (e.g., time, resource allocation) and relational aspects (e.g., communication process, families) shaped how ethical challenges surfaced and were managed. We call for greater awareness of contextual, institutional and social aspects and focus on these issues in training programs.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.215
GPT teacher head0.466
Teacher spread0.251 · 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 designQualitative
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

Citations9
Published2013
Admission routes1
Has abstractyes

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