Compassionate Care: Reflections of Oncology Nurses
Bibliographic record
Abstract
Compassionate care is elemental in maintaining excellence in the nursing profession. Yet compassion in some nurses can be depleted by repeated exposure to the suffering of others and result in compassion fatigue (CF) (Gustafsson & Hemberg, 2022). This paper explores why some exemplary nurses seem to forestall CF. Specifically, we investigate the attitudes of outstanding oncology nurses and the strategies they employ to sustain compassionate care in their professional lives. First, we searched through research reports from peer-reviewed journals and articles from grey literature to better understand compassionate nursing, compassion satisfaction (CS), and CF. Then we added reflections from oncology nurses who maintain compassion in their care through challenging working conditions, including during the COVID-19 pandemic. The literature reveals that exceptional oncology nurses can sustain empathy and compassion in their care due to their outlook on life, the specific strategies they use for self-care, and their unique approaches to caring for patients and families. The nurses’ reflections help us understand the coping strategies these nurses employ and how they mitigate the effects of CF and maintain an exemplary practice. We aim to encourage nurses and organizational leaders to use (and nurse educators to teach) strategies to help increase CS, reduce CF, and restore enthusiasm for practicing nursing.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".