Bibliographic record
Abstract
AIM: A discussion of how the construct of compassion fatigue is understood in nursing. BACKGROUND: Compassion fatigue is a topic commonly found in nursing literature. DESIGN: Discussion paper. DATA SOURCES: The literature from 1992-2012 on compassion fatigue was examined. The literature from 1998-2012 on compassion was examined. IMPLICATIONS FOR NURSING: There are multiple and diverse understandings and definitions of what compassion fatigue is. So much so, there are equally multiple, diverse and conflicting strategies to mitigate it. To understand better what compassion fatigue is, an examination of what compassion is was undertaken. Much is written that nurses are, or should be compassionate. Compassion is an archetype of nursing. However, there is little in the nursing literature defining what compassion is. Literature on compassion outside of nursing was then examined. There is a growing body of theory and research about compassion in other disciplines. None of the multiple definitions of nurse compassion fatigue match this understanding of compassion. The tools most often used to measure nurse compassion fatigue do not appear to measure the construct of compassion. CONCLUSION: To understand what nurse compassion fatigue is, we must first understand what nurse compassion is.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".