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Record W6884638785 · doi:10.11575/prism/33454

Integrating Spirituality as a Key Component of Patient Care

2015· other· en· W6884638785 on OpenAlexfundno aff

Bibliographic record

VenueUniversity of Calgary · 2015
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersUniversity of AlbertaAlberta Health Services
KeywordsSpiritualityHealth professionalsDistressMeaning (existential)Spiritual carePatient careFocus groupEmotional distress

Abstract

fetched live from OpenAlex

Patient care frequently focuses on physical aspects of disease management, with variable attention given to spiritual needs. And yet, patients indicate that spiritual suffering adds to distress associated with illness. Spirituality, broadly defined as that which gives meaning and purpose to a person’s life and connectedness to the significant or sacred, often becomes a central issue for patients. Growing evidence demonstrates that spirituality is important in patient care. Yet healthcare professionals (HCPs) do not always feel prepared to engage with patients about spiritual issues. In this project, HCPs attended an educational session focused on using the FICA Spiritual History Tool to integrate spirituality into patient care. Later, they incorporated the tool when caring for patients participating in the study. This research (1) explored the value of including spiritual history taking in clinical practice; (2) identified facilitators and barriers to incorporating spirituality into person-centred care; and (3) determined ways in which HCPs can effectively utilize spiritual history taking. Data were collected using focus groups and chart reviews. Findings indicate positive impacts at organizational, clinical/unit, professional/personal and patient levels when HCPs include spirituality in patient care. Recommendations are offered.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.025
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.003

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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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