Exploring Empathy in Nursing Practice: A Descriptive Study Among Nurses
Bibliographic record
Abstract
Background: Empathy is a core component of professional nursing practice, influencing patient outcomes, ethical behavior, and the quality of nurse-patient interactions. Despite its importance, empathy remains under-researched in challenging and culturally distinct settings, such as Mosul, Iraq.Objectives: This study aimed to assess the levels of empathy among nurses working in governmental hospitals in Mosul and to explore its relationship with emotional intelligence, moral sensitivity, and selected demographic and professional factors.Methods: A descriptive cross-sectional study was conducted with a sample of 319 registered nurses across eight governmental hospitals in Mosul. The Toronto Empathy Questionnaire (TEQ) was used to measure empathy levels. Data was collected through self-administered questionnaires between November and December 2024. Statistical analysis was performed using SPSS version 26, including descriptive and inferential statistics.Results: The mean empathy score among participants was 42.3 ± 7.6. High empathy was observed in 38.9% of the nurses, moderate in 42.9%, and low in 18.2%. Significant associations were found between empathy and gender (p = 0.005) and education level (p = 0.019). A moderate positive correlation was also observed between empathy and emotional intelligence (r = 0.41, p < 0.001), and between empathy and moral sensitivity.Conclusion: The study reveals a generally moderate to high level of empathy among nurses in Mosul’s public hospitals, which is influenced by emotional intelligence and moral sensitivity. Interventions focused on enhancing emotional regulation, reflective practice, and ethics education are recommended to strengthen empathetic capacity, particularly for nurses with lower scores.
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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 | 0.004 |
| 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 | 0.000 |
| 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".