One Cannot Pour From an Empty Cup: Compassion Fatigue, Burnout, Compassion Satisfaction, and Coping Among Child Life Specialists
Bibliographic record
Abstract
Objective: Child life specialists provide support across various medical units and are frequently exposed to high-intensity, stressful, or traumatic situations. As such, they are at risk of developing burnout and compassion fatigue; however, limited research has examined the relationship between professional well-being of child life specialists and use of coping strategies. The present study examined professional quality of life, including compassion satisfaction, compassion fatigue, and burnout. Method: This survey used responses from 196 child life specialists across the United States and Canada to examine whether professional quality of life varied based on reported coping strategies, frequency of supervision and consultation, or hospital units on which participants worked. Results: Results revealed use of avoidant coping strategies was associated with lower likelihood of compassion satisfaction and higher risk of compassion fatigue and burnout. On the other hand, more frequent consultation with colleagues was associated with higher likelihood of compassion satisfaction. Child life specialists who worked in hematology/oncology units reported higher risk of compassion fatigue than those on other medical units. Conclusion: This study provided several implications for practice to enhance child life specialists’ professional quality of life. Researchers should consider qualitative studies to better understand the professional quality of life of child life specialists in order to improve the delivery of quality, family-centered care. Disclosure Statement: No potential conflict of interest was reported by the author(s). Funding Statement: No funding sources were provided by the author(s).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".