Unearthing Predictors of Emotional Intelligence, Empathy, and Work Engagement Impacting Acute Care Nursing
Bibliographic record
Abstract
Background: Emotional intelligence (EI), empathy, and work engagement (WE) influence nursing performance and patient outcomes in acute care settings. This study examined EI, empathy, and WE predictors among nurses in these environments. Methods: A cross-sectional design was used with a convenience sample of 264 nurses recruited from three acute care hospitals. Participants completed the Utrecht Work Engagement Scale (UWES-17), the Toronto Empathy Questionnaire (TEQ), and the Wong and Law Emotional Intelligence Scale (WLEIS). Multiple linear regression analyses identified predictors for each variable. Results: Age, sex, education level, perceived empathy, and work engagement significantly predicted EI ( p < .05). Work engagement, perceived EI, and hospital site were significant predictors of empathy ( p < .05). Finally, EI, empathy, education level, and hospital site significantly predicted WE ( p < .05). Conclusion: Emotional intelligence, empathy, and work engagement are interconnected and influenced by individual and organizational factors. Nursing leadership should develop targeted interventions to enhance these traits, promoting better clinical performance and patient care outcomes in acute care settings.
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 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".