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Record W4321368519 · doi:10.1136/spcare-2023-scpsc.7

S2-3 Spirituality and palliative care: current evidence and future priorities?

2023· article· en· W4321368519 on OpenAlexvenueno aff
Karen E. Steinhauser

Bibliographic record

VenueSymposium · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsSpiritualityCoping (psychology)Psychological interventionPalliative careSpiritual careMeaning (existential)PsychologyHealth careDistressExistentialismNursingEngineering ethicsPsychotherapistMedicineAlternative medicineEpistemologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

<h3></h3> Research conducted over the past few decades has made significant strides towards illuminating the role of spirituality during serious illness. We know spirituality is integral to patient and family lives as a framework for meaning-making, coping and decision-making. When spiritual needs are met, quality of life and hospice utilization are higher and costs are lower. However, while the evidence base is growing, in quantity and rigor, the field lacks gold standard approaches to definitions, measurement and assessment. To move forward, we must improve our evidence base with regard to 1) What are the definitions of spirituality and religion, and identifying key domains of those constructs 2) What is the impact of those domains on key health care outcomes; 3) What are the unique issues associated with research design? 4) How do we best assess spiritual needs and spiritual well-being? And 5) What do we know about interventions to address spiritual and existential care distress and well-being? This paper presents a discussion of our evidence base to date with regard to these key issues. It also offers priorities for improving the evidence base of spirituality and palliative care, so that this key aspect of patient and family experience is more fully understood and met with comprehensive and rigorously approaches to care.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.510

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.064
GPT teacher head0.398
Teacher spread0.334 · 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
Published2023
Admission routes1
Has abstractyes

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