Making sense of pediatric death: An exploratory qualitative study of emotion management strategies applied by the pediatric intensive care unit interprofessional team
Bibliographic record
Abstract
Background: Caring for children at the end of life is a reality of practice in the pediatric intensive care unit (PICU). Learning how to make sense of death at work, and the emotions it entails, is necessary for all PICU professionals. Objective: To explore how PICU clinicians manage their emotions when encountering pediatric death at work. Design: Exploratory qualitative study grounded in interpretive phenomenology and the theoretical lens of emotional labor. We conducted one-time semi-structured interviews. Once transcribed, we inductively coded interview transcripts and subsequently generated themes through reflexive thematic analysis. Methods: = 13). Four participants self-identified as Black, Indigenous, and/or a person of color. Results: We generated four themes that influenced how clinicians managed emotions related to death in PICU: (1) Figuring it out on the job; (2) Reframing and rationalizing death; (3) Managing emotions as quality end-of-life care; (4) Navigating organizational constraints. Although clinicians shared many strategies and resources for managing emotions, the ability to apply these strategies was impacted by systemic constraints (e.g., pace of work, understaffing) and unequal access across professions to unit-level resources. Conclusion: Navigating pediatric death in the workplace requires skilled emotional labor, and clinician access to appropriate support to manage its impacts, which varies by unit culture and profession. PICU leaders should facilitate unit- and individual-level supports that are inclusive of all team members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".