Comprehensive meta-analysis of emotional intelligence
Bibliographic record
Abstract
BACKGROUND: Emotional intelligence refers to an individual's awareness of their emotions and their ability to effectively regulate them. Emotional intelligence also encompasses the ability to empathize with and establish meaningful relationships with others. OBJECTIVE: In this study, a comprehensive meta-analysis approach was employed to investigate the relationships between emotional intelligence and various factors including social support, organizational aspects, satisfaction, and stressors. METHODS: Moreover, the extent to which emotional intelligence influenced these factors was investigated and analyzed through meta-analysis. RESULTS: A data analysis revealed that emotional intelligence correlated positively with social support, organizational aspects, and satisfaction and negatively with stressors. CONCLUSIONS: These results suggest that organizations should adopt management strategies for enhancing the emotional intelligence of their employees, thereby strengthening their social support systems and their organizational cohesion and efficiency. To achieve this, organizations are advised to implement reasonable management systems and emotional management education and training to enable employees to effectively manage their emotions and understand the emotions of others. Subsequently, the job and life satisfaction of the employees can be enhanced and the negative effects of stressors can be mitigated.
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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 | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.026 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".