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Record W4390202536 · doi:10.30476/beat.2023.98919.1444

The Association between Occupational Burnout and Spiritual Well-being in Emergency Nurses: A Cross-Sectional Study.

2023· article· en· W4390202536 on OpenAlexaff
Hedayat Jafari, Rahmatollah Marzband, Mahsa Kamali, Mahmood Moosazadeh, Pooyan Ghorbani Vajargah, Samad Karkhah, Joseph Osuji, Behzad Davaribina

Bibliographic record

VenuePubMed · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsMount Royal University
Fundersnot available
KeywordsBurnoutMedicineCross-sectional studyScale (ratio)Family medicineEmergency departmentNursingClinical psychology

Abstract

fetched live from OpenAlex

Objective: This study evaluated the occupational burnout (OB) and spiritual well-being (SWB) of emergency nurses as well as the associations between these variables. Method: This cross-sectional study was conducted in six hospitals and emergency medical centers affiliated with Ardebil University of Medical Sciences (Ardebil, Iran), in 2020. Data were collected via socio-demographic, Spiritual Well-Being Scale (SWBS), and Maslach Burnout Inventory (MBI) questionnaires. Results: This study included 239 emergency department nurses. The mean age of the participants was 34.4±6.4 years. The mean of existential well-being and religious well-being was 40.3±8.7 and 41.0±9.2, respectively. The results indicated that moderate (P=0.007) and severe (P<0.001) personal accomplishment was a positive and significant predictor of the SWB in emergency department nurses. Conclusion: Proper planning and provision of suitable educational programs in the dimension of the SWB of nurses prevent the creation and continuation of OB and increase the self-efficacy and job satisfaction of emergency medical staff, resulting in better patient care.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.376
Teacher spread0.329 · 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 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

Citations2
Published2023
Admission routes1
Has abstractyes

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