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Record W4364353531 · doi:10.1097/spc.0000000000000646

Current practices in managing end-of-life existential suffering

2023· review· en· W4364353531 on OpenAlexaff
Michelle Di Risio, Alison Thompson

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2023
Typereview
Languageen
FieldPsychology
TopicPsychedelics and Drug Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPalliative carePsychological interventionExistentialismMedicineContext (archaeology)ModalitiesDistressSalience (neuroscience)PsychotherapistEnd-of-life carePsychologyPsychiatryNursingClinical psychology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Within the context of palliative care, existential suffering (ES) can be an exclusive source of suffering or intertwined with physical pain and/or psychological and spiritual suffering. With newly emerging modalities for addressing this phenomenon and its increasing salience given that many patients cite ES as a significant contributing factor to requests for hastened death, a review of recent interventions for addressing ES at the end of life is timely. RECENT FINDINGS: This review of newer approaches to dealing with ES in the palliative context suggests some promising new modalities and pharmacological interventions, such as brain stimulation and the use of psychedelics. The use of other pharmacological interventions, such as palliative sedation and lethal injections, solely for the alleviation of existential distress remains ethically controversial and difficult to disentangle from other forms of suffering, not least because a clear clinical definition of ES has yet to emerge in the literature. SUMMARY: The evaluation of end-of-life (EOL) ES mitigating tools should also consider how broader contexts, such as institutional arrangements and barriers, and cultural factors may influence the optimal management of dying persons' ES in the palliative care setting.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.479
GPT teacher head0.557
Teacher spread0.077 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2023
Admission routes1
Has abstractyes

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