Culturally sensitive palliative care in humanitarian action: Lessons from a critical interpretive synthesis of culture in palliative care literature
Bibliographic record
Abstract
OBJECTIVE: There is growing recognition of the importance of increasing preparedness for and the provision of palliative care in humanitarian crises. The primary objective of this review is to interpret the existing literature on culture and palliative care to query the recommendation that humanitarian healthcare providers, teams, and organizations integrate palliative care into their practice in ways that are attentive to and respectful of cultural differences. METHODS: A critical interpretive synthesis was applied to a systematic literature review guided by the PRISMA framework. Analysis was based on directed data extraction and was team based, to ensure rigor and consistency. RESULTS: In total, 112 articles covering 51 countries and 9 major worldviews met inclusion criteria. This literature describes culture as it influences perspectives on death and dying, expectations of palliative care, and challenges to providing culturally sensitive care. A key pattern highlighted in articles with respect to the culture and palliative care literature is that culture is invoked in this literature as a sort of catch-all for non-white, non-Christian, indigenous practices, and preferences for palliative care. It is important that humanitarian healthcare providers and organizations aiming to enact their commitment of respect for all persons through attention to potential culturally specific approaches to pain management, suffering, and dying in specific crisis settings do so without reproducing Othering and reductionistic understandings of what culturally sensitive care in humanitarian crises settings involves. SIGNIFICANCE OF RESULTS: This paper clarifies and unpacks the diverse influences of culture in palliative care with the goal of supporting the preparedness and capacity of humanitarian healthcare providers to provide palliative care. In doing so, it aids in thinking through what constitutes culturally sensitive practice when it comes to palliative care needs in humanitarian crises. Providing such care is particularly challenging but also tremendously important given that healthcare providers from diverse cultures are brought together under high stress conditions.
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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 | 0.272 | 0.348 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.007 | 0.011 |
| 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".