Intervening to improve compassion fatigue resiliency in nurse residents
Bibliographic record
Abstract
Nurses who are younger and new to the profession demonstrate higher prevalence of compassion fatigue compared to their more experienced counterparts. Accordingly, the Commission on Collegiate Nursing Education Standards for Accreditation recently required that nurse residency programs incorporate the teaching of strategies to prevent compassion fatigue in their learning experiences. This study examined the impact of a compassion fatigue resiliency intervention in new graduate nurse residents in two hospitals with nurse residency programs within a university health system. Compassion satisfaction and the two components of compassion fatigue (CF), secondary traumatic stress (STS) and burnout (BO), were measured at baseline and 2-month follow-up. Changes in mean scores and prevalence were reported. A statistically significant decrease in mean STS from baseline to follow-up was found ( p < .001). A mean increase in CS and decrease in BO were trending in the desired direction but were not statistically significant. As hypothesized, prevalence of CS increased and STS and BO decreased from baseline to 2-months post intervention.The results suggest that compassion fatigue interventions may be beneficial to nurse residents in decreasing CF symptoms and increasing CS early in their careers. More research is needed to understand the optimal timing and type of intervention.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".