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Record W2899267299

Could Depression and Loss of Dignity Correlate with Requesting Euthanasia and Physician-Assisted Suicide? A Look at the Research from the United States, Canada, and the Netherlands

2018· article· en· W2899267299 on OpenAlexaboutno aff
Jana Sahyouni

Bibliographic record

VenueHope College Digital Commons (Hope College) · 2018
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsnot available
Fundersnot available
KeywordsDignityDepression (economics)Death with dignityPsychiatryAssisted suicideMedicinePsychologyGerontologyFamily medicinePolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Euthanasia and physician-assisted suicide are legal in the Netherlands, Belgium, Luxembourg, Colombia, and Canada. Physician-assisted suicide alone is legal in Switzerland and within the U.S. in Oregon, Washington, Montana, California, and Vermont. Public support in the United States and the Netherlands for the “right to die” has steadily increased since 1950. This research seeks to uncover the underlying reasons that patients request euthanasia and physician-assisted suicide. Signs of psychological depression and loss of dignity appear to be the main reasons for considering euthanasia and physician-assisted suicide. In the United States, requests for euthanasia and physician-assisted suicide correlated most strongly with loss of autonomy, not being able to enjoy everyday life activities, and loss of dignity. In the Netherlands, more than half of euthanasia/physician-assisted suicide cases contained loss of dignity as one of the reasons. In Canada, the desire for death in the terminally ill was higher for people who had higher ratings of pain, lower family support, and depressive symptoms. Of these, depression is the best predictor of the desire to die. Palliative care that respects individual differences and psychological treatment that emphasizes the therapeutic alliance would provide people with more years of meaningful living.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.071
GPT teacher head0.313
Teacher spread0.242 · 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 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
Published2018
Admission routes1
Has abstractyes

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