Treatment withdrawal and distress: Recognising the need for better support in critical care—A scoping review
Bibliographic record
Abstract
BACKGROUND: Intensive care units house critically acute patients requiring extensive treatments and specialised care. Highly trained healthcare providers work tirelessly to perform life-sustaining measures, but when all possible treatment options have been exhausted, it sometimes becomes necessary to withdraw treatment. This process places critical care nurses and the interprofessional teams at the centre of emotionally and ethically challenging end-of-life care. OBJECTIVES: The objective of this review was to synthesise what is known about the types of moral, emotional, and psychological distress experienced by healthcare providers during treatment withdrawal in adult intensive care units and to summarise the support strategies described in the literature to mitigate these experiences. RESULTS: Nine studies met inclusion criteria, representing 883 healthcare professionals across eight countries. Emotional distress was linked to repeated exposure to death, patient-family relationships, and the act of extubation. Moral distress arose from perceived prolongation of suffering, contradictions with patient wishes, and exclusion from decision-making. None of the studies directly measured psychological distress, representing a critical gap. Nurses consistently reported the greatest burden, often coordinating care and supporting families while being excluded from withdrawal planning. Across all studies, institutionalised support strategies were absent, with providers relying on individual coping mechanisms. CONCLUSION: Withdrawing treatment is a task that can lead to emotional and moral distress of healthcare professionals. This review highlights the disconnect between predictable distress and the absence of systematic institutional support. Collaborative planning, standardised withdrawal protocols, mandatory breaks, and structured debriefing could help transform withdrawal experiences into opportunities for meaningful end-of-life care. IMPLICATION FOR PRACTICE: The findings suggest that institutionalising interprofessional collaboration, communication training, and postextubation debriefing could reduce moral distress and improve team resilience during terminal extubation procedures.
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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 | 0.033 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| 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".