The influence of trait-negative affect and compassion satisfaction on compassion fatigue in Australian nurses.
Bibliographic record
Abstract
For this study, we examined the nature of the unique relationships trait-negative affect and compassion satisfaction had with compassion fatigue and its components of secondary traumatic stress and burnout in 273 nurses from 1 metropolitan tertiary acute hospital in Western Australia. Participants completed the Professional Quality of Life Scale (Stamm, 2010), Depression Anxiety Stress Scale (Lovibond & Lovibond, 2004), and the State-Trait Anxiety Inventory (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983). Bivariate correlation and hierarchical regression analyses were performed to examine and investigate 4 hypotheses. The results demonstrate a clear differential pattern of relationships with secondary traumatic stress and burnout for both trait-negative affect and compassion satisfaction. Trait-negative affect was clearly the more important factor in terms of its contribution to overall compassion fatigue and secondary traumatic stress. In contrast, compassion satisfaction's unique protective relationship only related to burnout, and not secondary traumatic stress. The results are therefore consistent with the view that compassion satisfaction may be an important internal resource that protects against burnout, but is not directly influential in protecting against secondary traumatic stress for nurses working in an acute-care hospital environment. With the projected nursing workforce shortages in Australia, it is apparent that a further understanding is warranted of how such personal variables may work as protective and risk factors.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".