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Record W2170212886 · doi:10.1177/1049732307306921

Learning to Care for Spiritual Needs: Connecting Spiritually

2007· article· en· W2170212886 on OpenAlexaff
L. Elizabeth Hood, Joanne Olson, Marion Allen

Bibliographic record

VenueQualitative Health Research · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsUniversity of AlbertaAlberta Hospital Edmonton
Fundersnot available
KeywordsSpiritualityGrounded theoryPsychologyExperiential learningSpiritual careExperiential knowledgeConfusionProcess (computing)NursingSocial psychologyQualitative researchPedagogySociologyMedicineEpistemologyPsychoanalysis

Abstract

fetched live from OpenAlex

Despite mandates to provide spiritual care, confusion persists among nurses about spirituality, spiritual needs, and related roles. To discover how practicing nurses acquire knowledge for spiritual care, the authors chose a grounded theory design. They constantly compared and analyzed verbatim transcribed interview data to find the core variable, categories, and properties. Connection, manifesting as a state, act, or process, appeared throughout the data. Categories emerged as Needing Connection, Nurturing Connection, Learning Connection, and Living Connection. Nurses used a cyclical, intertwined, and progressive learning process of opening to, struggling with, and making connections between numerous discrete personal and professional experiences. Shifting attention between these interconnected experiences fueled knowledge acquisition. Whether referring to how nurses learn, what they do, or with whom, the theory Connecting Spiritually joined categories into a cumulative experiential learning process that explained how nurses learn to care for spiritual needs.

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)0.008
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.015
Scholarly communication0.0040.007
Open science0.0010.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.367
GPT teacher head0.620
Teacher spread0.254 · 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 designQualitative
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

Citations11
Published2007
Admission routes1
Has abstractyes

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