Comparison of Compassion Fatigue, Burnout and Compassion Satisfaction of Oncology-Hematology & Dialysis Nurses
Bibliographic record
Abstract
Background: It is known that health professionals who work with chronic patients for a long time are at risk in terms of compassion fatigue and burnout. Purpose: The study aims to compare the levels of compassion fatigue, burnout, and compassion satisfaction of nurses working on oncology-hematology and dialysis and determine the predictors. Methods: This study was carried out using descriptive and correlational research design. The research was conducted with 278 nurses. Participants were enrolled using a convenience sampling technique from the oncology–hematology inpatient services, outpatient chemotherapy units, and bone marrow transplant units and dialysis nurses of purposively selected hospitals in Istanbul, Turkey. Personal information form and Professional Quality of Life-IV. Potential predictors were analyzed using univariate analysis. We conducted multiple stepwise linear regression analyses to reveal the outcome variables. Results: A sub-analysis comparing the dialysis specialty with the oncology-hematology revealed that dialysis nurses report significantly higher compassion fatigue scores than oncology nurses. Working willingly in the current unit and the level of received social support were determined as predictors for all outcome variables. Conclusions: This is the first study comparing compassion fatigue, burnout, and compassion satisfaction levels of oncology-hematology and dialysis nurses. This study indicates the need for nurse managers to be aware of compassion fatigue and plan compassion fatigue prevention programs. In addition, this study guides the interventional studies to be conducted in the future.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".