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Record W4408059845 · doi:10.1017/s1478951525000173

“It seemed I was having a conversation with him”: Posthumous Dignity Therapy case series

2025· article· en· W4408059845 on OpenAlexaff
Miguel Julião, Carolina Simões, Harvey Max Chochinov

Bibliographic record

VenuePalliative & Supportive Care · 2025
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsUniversity of ManitobaResearch Institute in Oncology and HematologyCancerCare Manitoba
Fundersnot available
KeywordsDignityPsychosocialPalliative careGriefPsychotherapistTerminally illAnxietyConversationDistressMedicinePsychologyPsychological resilienceMental healthClinical psychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

OBJECTIVES: Dignity Therapy (DT) is a brief psychotherapeutic intervention designed to address the psychosocial and spiritual needs of terminally ill patients. Research demonstrates DT's efficacy in reducing dignity-related distress and alleviating psychosocial symptoms like depression and anxiety in terminally ill patients. Its application has been extended to nonterminal patients with chronic conditions, mental health challenges, and children nearing the end of life, with promising results. DT also benefits families and caregivers, promoting emotional resilience and facilitating grieving. However, the potential for proxy applications, such as posthumous DT (p-DT) - conducted by relatives after a patient's death or on behalf of individuals unable to participate - remains underexplored. METHODS: A case series report. RESULTS: This case series examines 3 relatives who engaged in p-DT, highlighting its feasibility and potential benefits. SIGNIFICANCE OF RESULTS: Findings suggest p-DT may serve as a valuable tool for bereavement support, warranting further research to expand its scope and accessibility.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.042
GPT teacher head0.329
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations2
Published2025
Admission routes1
Has abstractyes

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