Professional Quality of Life and Empathy Among Nurses in a Selected Hospital in the United Kingdom
Bibliographic record
Abstract
Nurses have a duty to care empathetically and compassionately for their patients. However, there are instances that their delivery of services could either induce satisfaction or rob the nurses of their compassion about their job that will eventually lead to burnout. This could contribute to lower quality patient care and patient satisfaction. It was deemed necessary by the researcher to understand how their Professional Quality of Life (PROQOL) which depicts satisfaction, fatigue and burnout has relationship with empathy and demographic profile. The study employed descriptive and correlational methods. The study used the PROQOL questionnaire and the Toronto Empathy Questionnaire to measure the variables included in the study. Referral sampling was utilized. The tool was administered to 150 employed nurses from the chosen NHS hospital in UK where Pearson R was used as the statistical treatment. Most of the respondents are predominantly 25-29 years old, female, single, with 5-9 years of experience, Filipino and working in medical wards. An average level was seen with the nurses’ PROQOL and empathy. CS is affected by age, sex and civil status and CF is affected by the two latter factors. Meanwhile, age is a factor for burnout. Demographic profile is not a variable that can impact empathy. The respondent’s PROQOL revealed a significant relationship with empathy of the nurses. Compassion fatigue and burnout showed a negative relationship while compassion satisfaction showed otherwise. Decreasing compassion fatigue and burnout and increasing compassion satisfaction can increase the degree of empathy among nurses. Support for nurses with continuous and adequate in-service trainings that could focus on self-care is necessary to develop the empathetic skills of the nurses.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".