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Record W2068664385 · doi:10.1177/0898010114524483

Integrating Spiritual Care into a Baccalaureate Nursing Program in Mainland China

2014· article· en· W2068664385 on OpenAlexaff
Hua Yuan, Caroline Porr

Bibliographic record

VenueJournal of Holistic Nursing · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSpiritualityMainland ChinaNursingSpiritual careNurse educationCurriculumHolistic nursingContext (archaeology)ChinaPsychologySociologyMedicinePedagogyPolitical scienceAlternative medicine

Abstract

fetched live from OpenAlex

Holistic nursing care takes into account individual, family, community and population well-being. At the level of individual well-being, the nurse considers biological, psychological, social, and spiritual factors. However, in Mainland China spiritual factors are not well understood by nursing students. And accordingly, nursing faculty and students are reluctant to broach the topic of spirituality because it is either unknown to students or students believe that the provision of spiritual care is beyond their capabilities. We wonder then, what can we do as nurse educators to integrate spiritual care into a baccalaureate nursing program in Mainland China? The purpose of this article is to propose the integration of Chinese sociocultural traditions (namely religious/spiritual practices) into undergraduate nursing curricula as a means to enter into dialogue about spiritual well-being, to promote spiritual care; and to fulfill the requirements of holistic nursing care. However, prior to discussing recommendations, an overview of the cultural context is in order. Thus, this article is constructed as follows: first, the complexity of Chinese society is briefly described; second, the historical evolution of nursing education in Mainland China is presented; and, third, strategies to integrate Chinese religious/spiritual practices into curricula are proposed.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.644
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.404
Teacher spread0.374 · 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 designOther design
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
Published2014
Admission routes1
Has abstractyes

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