Toward a comprehensive, theoretical model of compassion fatigue: <scp>A</scp>n integrative literature review
Bibliographic record
Abstract
This study was an integrative literature review in relation to compassion fatigue models, appraising these models, and developing a comprehensive theoretical model of compassion fatigue. A systematic search on PubMed, EbscoHost (Academic Search Premier, E-Journals, Medline, PsycINFO, Health Source Nursing/Academic Edition, CINAHL, MasterFILE Premier and Health Source Consumer Edition), gray literature, and manual searches of included reference lists was conducted in 2016. The studies (n = 11) were analyzed, and the strengths and limitations of the compassion fatigue models identified. We further built on these models through the application of the conservation of resources theory and the social neuroscience of empathy. The compassion fatigue model shows that it is not empathy that puts nurses at risk of developing compassion fatigue, but rather a lack of resources, inadequate positive feedback, and the nurse's response to personal distress. By acting on these three aspects, the risk of developing compassion fatigue can be addressed, which could improve the retention of a compassionate and committed nurse workforce.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".