"Bridging the Gap: Emotional Intelligence, Job Satisfaction, and Their Influence on Nurses' Turnover Intentions"
Bibliographic record
Abstract
This study investigates the relationship between Emotional Intelligence (EI), Job Satisfaction (JS), and Turnover Intentions (TI) among nurses, aiming to address critical gaps in existing literature. Demographic analysis of 177 respondents reveals a predominantly female, young, and less experienced workforce within the nursing profession. Using correlation and regression analyses, the study finds a robust positive correlation between EI and JS, with EI explaining nearly half of the variance in JS. Additionally, a significant negative correlation is identified between JS and TI, highlighting the important role of JS in mitigating nurses' intentions to leave their jobs. Furthermore, the study uncovers a moderate negative correlation between EI and TI, underscoring the potential of EI to influence nurses' turnover intentions. However, the findings also suggest the presence of unexplored factors impacting nurses' intentions to leave. This study provides valuable insights for healthcare organizations to foster EI skills among nurses, enhance JS, and ultimately reduce turnover rates. Further research is recommended to comprehensively understand the complex determinants of turnover intentions in nursing contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".