Intensive care clinicians’ experiences of palliative withdrawal of mechanical ventilation: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: To explore intensive care unit (ICU) clinicians' experiences of withdrawing mechanical ventilation during end-of-life care. DESIGN: An exploratory qualitative design was used, with data collected via semistructured, face-to-face online interviews and analysed using reflexive thematic analysis. PARTICIPANTS: We recruited ICU clinicians from two hospitals within the West Midlands region of the UK. DATA COLLECTION: Semistructured, face-to-face online interviews were used to explore experiences with limitation of life-sustaining treatments in ICU, decision-making and practices for withdrawing mechanical ventilation. FINDINGS: 22 ICU clinicians were interviewed (Physiotherapist=1, Advanced Critical Care Practitioners=4, Physicians=9 and Nurses=8), of which 13 were women (59%). Four themes were developed. (1) Multilayered communication: effective communication was key in planning withdrawal and informing family members, with conflicts arising from cultural differences. (2) Considerations regarding the mode of withdrawing invasive mechanical ventilation: clinicians expressed differing preferences for the method of mechanical ventilation withdrawal. (3) Multiprofessional teamwork: collaborative teamwork was vital, with palliative care practitioners consulted during conflicts or challenging symptoms. (4) Clinicians' feelings and impact: clinicians empathised with families and experienced psychological burden. CONCLUSIONS: Physician preferences influence the withdrawal process, which is communicated within the multidisciplinary team. Clear protocols can help reduce ambiguity and support less experienced clinicians. Reflection on these practices may help mitigate burnout and compassion fatigue. Further research should examine the effects of physician demographics and patient cultural diversity on the withdrawal process.
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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".