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Record W4385752216 · doi:10.1017/s1478951523001177

Cost considerations for implementing dignity therapy in palliative care: Insights and implications

2023· article· en· W4385752216 on OpenAlexaff
Raed Al Yacoub, Andrea P. Rangel, Adriana Shum-Jimenez, Amelia Greenlee, Yingwei Yao, Tasha M. Schoppee, George Fitchett, George Handzo, Harvey Max Chochinov, Linda L. Emanuel, Sheri Kittelson, Diana J. Wilkie

Bibliographic record

VenuePalliative & Supportive Care · 2023
Typearticle
Languageen
FieldMedicine
TopicPatient Dignity and Privacy
Canadian institutionsUniversity of ManitobaCancerCare ManitobaCanadian Hospice Palliative Care Association
FundersNational Institute of Nursing ResearchNational Cancer InstituteNational Institutes of Health
KeywordsDignityNursingPalliative careRandomized controlled trialMeaning (existential)Clinical trialMedicinePsychologyPsychotherapistInternal medicinePolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: Despite the clinical use of dignity therapy (DT) to enhance end-of-life experiences and promote an increased sense of meaning and purpose, little is known about the cost in practice settings. The aim is to examine the costs of implementing DT, including transcriptions, editing of legacy document, and dignity-therapists' time for interviews/patient's validation. METHODS: Analysis of a prior six-site, randomized controlled trial with a stepped-wedge design and chaplains or nurses delivering the DT. RESULTS: The mean cost per transcript was $84.30 (SD = 24.0), and the mean time required for transcription was 52.3 minutes (SD = 14.7). Chaplain interviews were more expensive and longer than nurse interviews. The mean cost and time required for transcription varied across the study sites. The typical total cost for each DT protocol was $331-$356. SIGNIFICANCE OF RESULTS: DT implementation costs varied by provider type and study site. The study's findings will be useful for translating DT in clinical practice and future research.

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)0.001
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 score0.078
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.232
GPT teacher head0.421
Teacher spread0.188 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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