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Record W1916105962 · doi:10.1111/nhs.12210

Concordance between nurses' perception of their ability to provide spiritual care and the identified spiritual needs of hospitalized patients: A cross‐sectional observational study

2015· article· en· W1916105962 on OpenAlexaff
Lifen Wu, Malcolm Koo, Hui‐Chen Tseng, Yu‐Chen Liao, Yuh‐Min Chen

Bibliographic record

VenueNursing and Health Sciences · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsPublic Health OntarioUniversity of Toronto
FundersNational University of Science and TechnologyNational University of Sciences and TechnologyNational Science Council
KeywordsObservational studyConcordanceCross-sectional studyPerceptionNursingSpiritual careMedicinePsychologyFamily medicineSpiritualityAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

Spiritual care is essential to the well-being of patients, and nurses provide spiritual care as a fundamental part of nursing practice. In this study, we investigated the spiritual care needs of hospitalized patients to determine whether the perceived knowledge of nurses corresponded with these spiritual care needs. A cross-sectional study was conducted on 1351 hospitalized patients and 200 registered nurses recruited from a medical center in central Taiwan. A questionnaire, including the 21-item Spiritual Care Needs Inventory (patient and nurse version) and basic demographic information, was distributed to eligible participants. The top three items of the spiritual care needs expressed by the hospitalized patients were respect for privacy and dignity, showing concern, and guidance in gaining a sense of hope in life; the percentages of nurses not knowing how to provide these spiritual care needs were 0%, 1%, and 15%, respectively. The spiritual care needs of patients showed a significant relationship with the knowledge of nurses, suggesting that the perceived knowledge of the nurses generally corresponded with the spiritual care items that the patients required most.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
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 head0.156
GPT teacher head0.461
Teacher spread0.305 · 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 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

Citations25
Published2015
Admission routes1
Has abstractyes

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