Compassion Fatigue and Empathy among Nurses Working in Adult Intensive Care Units, PGIMER, Chandigarh 2019–20
Bibliographic record
Abstract
Objectives: To assess and compare the level of empathy and compassion fatigue among nurses working in the adult intensive care units. Methodology: A descriptive-comparative study design was adopted and total 120 nurses (Male= 60, Female= 60) were purposively selected. Participant’s information questionnaire, Professionals quality of life scale and Toronto empathy questionnaire were used for the data collection. Research setting was adult intensive care units of PGIMER, Chandigarh, India. The data were analyzed using SPSS (version 20). Result: Mean age of the male and female participants was 32.25 and 30.43years respectively. Most of the nurses were graduates and more than half had 1–5years intensive care unit working experience. Level of empathy was significantly higher (0.05) in female nurses as compared to male nurses. Correlation analysis showed that empathy had significant and positive association with compassion satisfaction and had negative relationship with burnout and secondary traumatic stress. Highly significant; negative relationship exists between compassion satisfaction and burnout. Burnout and secondary traumatic stress showed significant and positive association with each other. Conclusion: Empathetic and compassionate nursing professionals would be able to handle fatalistic aspects of exposure to mankind sufferings and illnesses. Negative association of burnout with compassion satisfaction and empathy will also contribute towards patient care provided by nurses in intensive care settings. Keywords: Empathy, compassion satisfaction, burnout, secondary traumatic stress Cite this Article Baljinder Kaur, Hema Ram, Lovepreet Kaur, Rajendra Yadav, Shikha Pandey, Monika Dutta, Prabhjot Kaur, Navneet Dhaliwal. Compassion Fatigue and Empathy among Nurses Working in Adult Intensive Care Units, PGIMER, Chandigarh 2019–20. Research & Reviews: A Journal of Health Professions. 2020; 10(3): 19–29p.
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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 | 0.003 |
| 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 | 0.000 |
| 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".