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Record W4387824282 · doi:10.12968/ijpn.2023.29.10.487

Spiritual care competence, moral distress and job satisfaction among Iranian oncology nurses

2023· article· en· W4387824282 on OpenAlexaff
Arpi Manookian, Javad Nadali, Shahrzad Ghiyasvandian, Kathryn Weaver, Shima Haghani, Anahita Divani

Bibliographic record

VenueInternational Journal of Palliative Nursing · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsCompetence (human resources)Job satisfactionNursingMedicineDistressPsychologyFamily medicineClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Nurses have a crucial role in identifying spiritual needs and providing spiritual care to patients living with cancer. AIM: This study evaluated Iranian oncology nurses' spiritual care competence and its relationship with job satisfaction and moral distress. METHOD: This cross-sectional study was conducted on 280 Iranian oncology nurses in 2020 using four questionnaires: demographic questionnaires, the Spiritual Care Competence Questionnaire (SCCQ), the Minnesota Job Satisfaction Questionnaire (MSQ) and the nurses' Moral Distress Questionnaire (MDS-R). FINDINGS: The mean scores indicated a medium to high Spiritual Care Competence (SCC), mild to moderate moral distress and high job satisfaction. There was a positive correlation between SCC and external job satisfaction (r=184, p<0.05) and a negative correlation between SCC and moral distress (r=-0.356, p<0.05). CONCLUSIONS: SCC diminishes with decreasing external job satisfaction and increasing moral distress. To improve the SCC of nurses working with patients living with cancer, it is recommended that nursing managers and policymakers revise the organisational policies to tackle the obstacles and consider the related factors to provide an ethical climate, implement quality spiritual care and increase job satisfaction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.056
GPT teacher head0.430
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 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

Citations18
Published2023
Admission routes1
Has abstractyes

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