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Record W4224325566 · doi:10.1080/0803706x.2022.2032332

Reflections on dying patients, hospices, assisted suicide, and euthanasia

2022· article· en· W4224325566 on OpenAlexaboutno aff
Christer Sjödin

Bibliographic record

VenueInternational Forum of Psychoanalysis · 2022
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsnot available
Fundersnot available
KeywordsAssisted suicidePalliative carePsychologyReciprocity (cultural anthropology)CriminologySocial psychologyPsychiatryMedicineNursing

Abstract

fetched live from OpenAlex

My parents’ death struggle, my clinical work with dying patients, the euthanasia of Freud, and a fear of dementia form the background to my reflections on dying patients, hospices, assisted suicide, and euthanasia. The change in public opinion has resulted in a displacement from Nazi crimes to the present focus on the right to self-determination. Consequently, a law allowing assisted suicide or euthanasia has been adopted in several locations, such as Oregon in the USA, the Benelux countries, Switzerland, and Canada. The fear of suffering, hopelessness, and inability are strong arguments to allow euthanasia and aided suicide. A compelling case against it is its negative social consequences, the infringement into the private sphere when the sick person and their family must decide if they are willing to accept assisted suicide or euthanasia. Although the “right to death” provides freedom to some, for others it is a forced choice that interferes with the dying process. I conclude by highlighting the palliative model, wherein death is perceived as a part of an individual’s life and as a normal process, although this task is hard for the family to contain, especially when the dying person is in pain and agony. Dying is not merely an individual process. It affects the whole family as well as the future generations’ views on reciprocity and responsibility.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.134
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.104
GPT teacher head0.407
Teacher spread0.304 · 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
Published2022
Admission routes1
Has abstractyes

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