Panacea Or Placebo? An Evaluation of the Value of Emotional Intelligence in Healthcare Workers
Bibliographic record
Abstract
The purpose of this study was to empirically investigate the relationship between emotional intelligence and desirable nursing behaviors, measured as organizational citizenship beehavior (OCB). We used Mayer and Salovey's (1997) four-dimensional model of emotional intelligence and Organ's (1988) OCB construct to test the EI-OCB relationships. Using a sample of 137 clinical nurses, and analyzing the data with hierarchical multiple regressions, we obtained results indicating that the EI dimension perceiving emotion was linked to conscientiousness, and facilitating thinking wvas linked to civic virtue. Managing emotion was linked to conscientiousness, civic virtue, altruism and courtesy. There were no relationships between facilitating thinking and the OCB dimensions. Results suggest that EI may increase conscientiousness in performing nursing duties, and in the levels of involvement and participation in hospital affairs. Higher levels of emotional intelligence may also increase altruistic activities and discretionary coordinating efforts. However, there is no reason to expect that a poor work climate, and grieving, complaining behaviors will respond positively to increasing EI. Managers should realize that efforts to improve EI may not provide global results.
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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 | 0.028 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".