Addressing Compassion Fatigue in Trauma Emergency and Intensive Care Settings: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Emergency and intensive care health care professionals are experiencing exhaustion and helplessness, which may cause compassion fatigue. Unaddressed compassion fatigue impacts staff morale and patient safety. Structured debriefing sessions may reduce compassion fatigue by providing social support and increasing job satisfaction. OBJECTIVE: To investigate the feasibility of a 12-week pilot of structured debriefing sessions and its impact on compassion fatigue experienced by emergency and intensive care health care professionals after patient death. METHODS: In this 12-week pilot study (March 2021 to May 2021), we used a preintervention/postintervention design to determine the feasibility of structured debriefing among trauma health care professionals experiencing patient death in an urban, academic, 300-bed, Midwest, Level II trauma center. Compassion fatigue was measured using the Professional Quality of Life Measure survey. Univariate descriptive statistics, independent unpaired t tests, and χ2 tests examined the intervention impact. RESULTS: Fifty-six health care professionals participated in 20 debriefing sessions during the 12-week intervention: 37 (80%) registered nurses, 10 (5.6%) respiratory therapists, and 5 (11.2%) nursing assistants or emergency medical technicians. The debriefings covered nearly half of all patient deaths (38%). No significant differences were seen in burnout (M = 25.5, SD = 5.4, p = .47), secondary traumatic stress (M = 23.9, SD = 5.6, p = .99), or compassion satisfaction (M = 36.8, SD = 6.4, p = .61). CONCLUSIONS: Structured debriefings to address compassion fatigue among trauma health care professionals are feasible, but further research on effectiveness is needed. Administration-provided emotional support strategies may assist health care professionals in processing work-related stress.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".