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Emotional Intelligence and Job Satisfaction Among Nephrology Nurses Working in Acute and Chronic Hemodialysis Settings

2024· article· en· W4406503466 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNephrology Nursing Journal · 2024
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsShaughnessy Hospital
Fundersnot available
KeywordsJob satisfactionEmotional intelligenceEconomic shortageBurnoutHemodialysisMedicineDialysisNephrologyNursingJob attitudePsychologyNursing shortageClinical psychologyJob performanceInternal medicineSocial psychologyNurse education

Abstract

fetched live from OpenAlex

Nephrology nurses working in hemodialysis units face unique challenges managing multiple patients - an experience often contributing to higher levels of burnout and stress, and potentially lower job satisfaction and retention rates, exacerbating the existing nursing shortage in dialysis settings. Targeted strategies are essential to improve job satisfaction. In this study, we explored the relationship between emotional intelligence and job satisfaction among nephrology nurses working in acute and chronic hemodialysis settings. A quantitative, non-experimental, descriptive, correlational design was used. There was a statistically significant positive correlation between emotional intelligence and job satisfaction, suggesting that heightened levels of emotional intelligence are associated with increased job satisfaction among nurses. Recommendations for enhancing emotional intelligence are discussed.

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.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.327
Teacher spread0.304 · 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