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Record W2993173971 · doi:10.7739/jkafn.2019.26.4.240

Effects of a Spirituality Promotion Program on Spirituality, Empathy and Stress in Nursing Students

2019· article· en· W2993173971 on OpenAlexaboutno aff
Seok‐Jung Kang, Jinsun Yong

Bibliographic record

VenueJournal of Korean Academy of Fundamentals of Nursing · 2019
Typearticle
Languageen
FieldNursing
TopicHealthcare Education and Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsSpiritualityEmpathyNursingPromotion (chess)PsychologyClinical psychologyTest (biology)Holistic nursingMedicineSocial psychologyAlternative medicine

Abstract

fetched live from OpenAlex

The purpose of this study was to examine the effects of a spirituality promotion program on spirituality, empathy and stress in nursing students. Methods: This study used one-group pretest-posttest design. Participants were 162 nursing students who participated in the spirituality program at C University in Seoul between 2014 and 2016. The effects of this study were measured using the Spirituality Assessment Scale, Toronto Empathy Questionnaire and Perceived Stress Scale-10. Data were analyzed using paired t-test and Wilcoxon signed rank test. Results: Spirituality increased significantly (Z=-8.06, p<.001), empathy also increased significantly (Z=-2.05, p=.040) and perceived stress decreased significantly (t=5.59, p<.001) after the spirituality promotion program. Conclusion: Results show that the spirituality promotion program is an effective intervention to improve spirituality and empathy and reduce stress for nursing students. Therefore, this study proposes utilization of this spirituality promotion program with nursing students so that they can take care of themselves and develop the ability to perform holistic nursing care for patients.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.446
Teacher spread0.400 · 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 designNon-randomized trial
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

Citations7
Published2019
Admission routes1
Has abstractyes

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