Alexithymia Levels in Oncology Nurses and Their Impact on Occupational Burnout, Compassion Satisfaction and Compassion Fatigue: A Cross-Sectional Study
Bibliographic record
Abstract
Objective: Nurses working with cancer patients face unique psychological and emotional challenges due to the intense nature of their work environment. Alexithymia, characterized by difficulty in identifying and expressing emotions, may significantly impact these nurses' professional well-being. This study aims to explore the correlation between the levels of alexithymia and the experiences of occupational burnout, compassion satisfaction, and compassion fatigue among nurses in oncology settings. Material and Methods: A descriptive and correlational research design was employed at Gaziantep University Şahinbey Research and Application Hospital. The study involved 80 nurses from different oncology services. Assessment tools included the Toronto Alexithymia Scale, Maslach Burnout Inventory, Compassion Satisfaction Scale, and Compassion Fatigue-Short Scale. Results: The study found a moderate positive correlation between alexithymia and occupational burnout (r=0.418). Additionally, significant relationships were noted between burnout and compassion fatigue, and various demographic factors (age, marital status, voluntary work in oncology) were found to influence alexithymia and burnout levels. Conclusion: The findings highlight the need for targeted psychological support and interventions for nurses in oncology settings, given the identified relationship between alexithymia, burnout, and compassion fatigue. This study contributes to the understanding of the emotional dynamics in high-stress healthcare environments and emphasizes the importance of addressing these issues to improve patient care quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".