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Record W4415705942 · doi:10.1080/07481187.2025.2580258

The experience of dignity in the context of medical assistance in dying: A phenomenological and hermeneutic study

2025· article· en· W4415705942 on OpenAlexafffund
Isabelle Martineau, Johanne Hébert, Naïma Hamrouni, Dominique Girard

Bibliographic record

VenueDeath Studies · 2025
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsInnovation and Economic Development Trois RivièresCégep de RimouskiUniversité du Québec à Trois-RivièresUniversité du Québec à Rimouski
FundersSocial Sciences and Humanities Research CouncilMinistère de l'Éducation et de l'Enseignement supérieurRéseau de recherche portant sur les interventions en sciences infirmières du Québec
KeywordsDignityAutonomyContext (archaeology)AppealEmbodied cognitionLived experiencePersonhoodBioethics

Abstract

fetched live from OpenAlex

Medical assistance in dying has become a significant topic of debate in Western societies, where legalization is expanding. A key feature of this debate is the appeal to dignity, invoked by both proponents and opponents. Two dominant conceptions of dignity emerge from the literature: ontological dignity and dignity based on autonomy. Public discourse often draws on these frameworks. This study aims to move beyond this binary by exploring dignity through the lived experiences of individuals who choose medical assistance in dying. Using a phenomenological-hermeneutic approach, interviews were conducted with six patients considering medical assistance in dying and six bereaved individuals. Five key themes emerged: 1) dignity-autonomy, 2) dignity-identity, 3) dignity without and with others, 4) suffering and doubt, 5) embodied and contextualized experience. While autonomy initially appears central, further analysis, informed by Ricoeur's philosophical concepts, highlights a relational dimension that refines a purely autonomy-centered understanding of dignity.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.138
Threshold uncertainty score0.151

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.152
GPT teacher head0.419
Teacher spread0.267 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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