A Review of Palliative and Hospice Care in the Context of Islam: Dying with Faith and Family
Bibliographic record
Abstract
BACKGROUND: By starting to understand Muslim culture, we can seek common ground with Islamic culture within the American experience and bridge opportunities for better palliative and hospice care here and in Middle Eastern countries. The United States, Canada, and Europe are education hubs for Middle Eastern students, creating an opportunity for the palliative and hospice care philosophy to gain access by proxy to populations of terminally ill patients who can benefit from end-of-life care. OBJECTIVE: The aim was to assess the state of research and knowledge about palliative and hospice care within the context of Muslim culture and religion. RESULTS: Within the guide of the key search terms, we learned that at a glance, over 100 articles meet the search criteria, but after a closer inspection, only a portion actually contributed knowledge to the literature. This confirmed the need for research in this vein. More importantly, we posit that once the layers of culture, religion, norms, and nationality are removed, human beings share a kinship based on family, spirituality, death and dying, and fear of pain. This is evident when we compare the Middle Eastern end-of-life experience with the western end-of-life care. CONCLUSIONS: A true opportunity to make a lasting impact at the patient level exists for palliative and hospice care researchers if we seek to understand, gain knowledge, and respect Muslim culture and Islamic issues 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".