Care for Dying Children and Their Families in the PICU: Promoting Clinician Education, Support, and Resilience
Bibliographic record
Abstract
OBJECTIVES: To describe the consequences of workplace stressors on healthcare clinicians in PICU, and strategies for personal well-being, and professional effectiveness in providing high-quality end-of-life care. DATA SOURCES: Literature review, clinical experience, and expert opinion. STUDY SELECTION: A sampling of foundational and current evidence was accessed. DATA SYNTHESIS: Narrative review and experiential reflection. CONCLUSIONS: The well-being of healthcare clinicians in the PICU influences the day-to-day quality and effectiveness of patient care, team functioning, and the retention of skilled individuals in the PICU workforce. End-of-life care, including decision making, can be complicated. Both are major stressors for PICU staff that can lead to adverse personal and professional consequences. Overresponsiveness to routine stressors may be seen in those with moral distress, and underresponsiveness may be seen in those with compassion fatigue or burnout. Ideally, all healthcare professionals in PICU can rise to the day-to-day workplace challenges-responding in an adaptive, effective manner. Strategies to proactively increase resilience and well-being include self-awareness, self-care, situational awareness, and education to increase confidence and skills for providing end-of-life care. Reactive strategies include case conferences, prebriefings in ongoing preidentified situations, debriefings, and other postevent meetings. Nurturing a culture of practice that acknowledges the emotional impacts of pediatric critical care work and celebrates the shared experiences of families and clinicians to build resilient, effective, and professionally fulfilled healthcare professionals thus enabling the provision of high-quality end-of-life care for children and their families.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".