Empathy, Resilience, and Psychological Well-Being of Nurses in Special Areas
Bibliographic record
Abstract
Compassionate relationships between patients and nurses give a sense of accomplishment to healthcare professionals. However, continuous exposure to emotionally charged situations affects the nurses' psychological well-being, leading to exhaustion and burnout. In vein, this study assessed empathy and resilience in the psychological well-being of nurses. in Amai Pakpak Medical Center in Marawi City. The descriptive-correlational design was used in the study, with the respondents the 120 nurses assigned to special hospital units. They were selected through cluster sampling, and standardized questionnaires such as the Toronto Empathy Questionnaire, Nicholson McBride’s Resilience Questionnaire, and Ryff’s Psychological Well-Being Scale were employed in gathering the data. In addition, mean, Standard Deviation, Pearson Product- Moment Correlation Coefficient, and Regression Analysis were used in analyzing the data gathered. The findings revealed that the respondents' high levels of empathy and resilience influenced their psychological well-being. However, resilience predicted the nurses’ emotional and overall functioning. Thus, overcoming work-related challenges determines nurses’strong disposition while caring for their patients. Keywords : emotional functioning, patient-nurse relationship, self-acceptance, strong disposition, work-related challenges
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".