Exploring the quality of end-of-life care in the intensive care unit: a qualitative multiple case study approach with family members of Muslim patients
Bibliographic record
Abstract
OBJECTIVE: Muslim patients are an underrepresented community in end-of-life care research, with little evidence around factors that influence the quality and experience of care. The aim of this study was to explore the quality of end-of-life care in the intensive care unit (ICU) from the perspective of next-of-kin family members of Muslim patients. DESIGN: A qualitative multiple case study design using semistructured interviews with family members of Muslim patients. Healthcare providers' notes from the patient medical record were also used to contextualise the patient care experience and medical journey. Data were analysed using deductive content analysis. SETTING: A large academic tertiary care hospital in Toronto, Canada. PARTICIPANTS: Next-of-kin family members of Muslim patients (≥18 years) who were admitted to the ICU and died in hospital between October 2019 and December 2021. RESULTS: Family members of five Muslim patients of South Asian descent were recruited for this study. Four central themes were identified: (1) trust and confidence in the healthcare team overseen by medical experts; (2) quality communication with medical experts; (3) achieving patient goals of care; and (4) dignity of care through cultural respect and emotional support. Culture, religion and religiosity did not appear to have a major influence on the medical decision-making process. Communication, emotional and psychological well-being for patients, and cultural respect were identified as possible areas for quality improvement. CONCLUSIONS: Our findings identified central themes in the quality of end-of-life care for families of seriously ill Muslim patients. These insights can inform clinical processes and interventions to improve the quality of care and reduce psychological and emotional burden at the end of life.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 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".