Unravelling complex choices: multi-stakeholder perceptions on dialysis withdrawal and end-of-life care in kidney disease
Bibliographic record
Abstract
BACKGROUND: For patients on dialysis with poor quality of life and prognosis, dialysis withdrawal and subsequent transition to palliative care is recommended. This study aims to understand multi-stakeholder perspectives regarding dialysis withdrawal and identify their information needs and support for decision-making regarding withdrawing from dialysis and end-of-life care. METHODS: Participants were recruited through purposive sampling from eight dialysis centers and two public hospitals in Singapore. Semi-structured in-depth interviews were conducted with 10 patients on dialysis, 8 family caregivers, and 16 renal healthcare providers. They were held in-person at dialysis clinics with patients and caregivers, and virtually via video-conferencing with healthcare providers. Interviews were audio-recorded, transcribed, and thematically analyzed. The Ottawa Decision Support Framework's decisional-needs manual was used as a guide for data collection and analysis, with two independent team members coding the data. RESULTS: Four themes reflecting perceptions and support for decision-making were identified: a) poor knowledge and fatalistic perceptions; b) inadequate resources and support for decision-making; c) complexity of decision-making, unclear timing, and unpreparedness; and d) internal emotions of decisional conflict and regret. Participants displayed limited awareness of dialysis withdrawal and palliative care, often perceiving dialysis withdrawal as medical abandonment. Patient preferences regarding decision-making ranged from autonomous control to physician or family-delegated choices. Cultural factors contributed to hesitancy and reluctance to discuss end-of-life matters, resulting in a lack of conversations between patients and providers, as well as between patients and their caregivers. CONCLUSIONS: Decision-making for dialysis withdrawal is complicated, exacerbated by a lack of awareness and conversations on end-of-life care among patients, caregivers, and providers. These findings emphasize the need for a culturally-sensitive tool that informs and prepares patients and their caregivers to navigate decisions about dialysis withdrawal and the transition to palliative care. Such a tool could bridge information gaps and stimulate meaningful conversations, fostering informed and culturally aligned decisions during this critical juncture of care.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".