Beyond technology, drips, and machines: Moral distress in PICU nurses caring for end‐of‐life patients
Bibliographic record
Abstract
Moral distress is an experience of profound moral compromise with deeply impactful and potentially long-term consequences to the individual. Critical care areas are fraught with ethical issues, and end-of-life care has been associated with numerous incidences of moral distress among nurses. One such area where the dichotomy of life and death seems to be at its sharpest is in the pediatric intensive care unit. The purpose of this study was to understand the moral distress experiences of pediatric intensive care nurses when caring for pediatric patients at the end of life. A secondary analysis was undertaken of seven transcripts from registered nurses across six Canadian pediatric intensive care units and produced three themes: under prioritization of child patient dignity, burden of insider knowledge, and environmental constraints on nursing roles and responsibilities. When caring for patients at the end of life, nurses experienced moral distress when a dignified death was not realized. Furthermore, despite interprofessional collaboration efforts in Canada, the concept of silo mentality persists and contributes to moral distress. Organizational involvement is needed to address moral distress in pediatric intensive care nurses both to achieve a dignified death for child patients and in addressing silo mentality.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".