Muslim patients in the U.S. confronting challenges regarding end-of-life and palliative care: the experiences and roles of hospital chaplains
Bibliographic record
Abstract
INTRODUCTION: Hospital chaplains aid patients confronting challenges related to palliative and end-of-life care, but relatively little is known about how chaplains view and respond to such needs among Muslim patients, and how well. METHODS: Telephone qualitative interviews of ~ 1 h each were conducted with 23 chaplains and analyzed. RESULTS: Both Muslim and non-Muslim chaplains raised issues concerning Islam among chaplains, doctors and patients, particularly challenges and misunderstandings between non-Muslim providers and Muslim patients, especially at the end-of-life, often due to a lack of knowledge of Islam, and misunderstanding and differences in perspectives. Due to broader societal Islamophobia, Muslim patients may fear or face discrimination, and thus not disclose their religion in the hospital. Confusion can arise among Muslim patients and families about what their faith permits regarding end-of-life care and pain management, and how to interpret and apply their religious beliefs in hospitals. Muslims hail from different countries, but providers may not fully grasp how these patients' cultural practices may also vary. Chaplains can help address these challenges, playing key roles in mediating tensions and working to counteract Muslim patients' fears, and express support. Yet many Muslim immigrants don't know what "chaplaincy" is and/or prefer a chaplain of their own faith. Muslim chaplains can play vital roles, having expertise that can heighten trust, and educating non-Muslim colleagues, providing in-depth understanding of Islam (e.g., highlighting how Islam is related to Judaism and Christianity) and correcting misconceptions among colleagues. Hospitals without a Muslim chaplain can draw on local community imams. CONCLUSIONS: These data highlight how mutual sets of misunderstandings, especially concerning patients' and families' decisions about end-of-life care and pain management, can emerge among Muslim patients and non-Muslim staff that chaplains can help mediate. Non-Muslim chaplains and providers should seek to learn more about Islam. Muslim patients and families may also benefit from enhanced education and awareness of chaplains' availability and scope, and of pain management and end-of-life options. These data thus have several critical implications for future practice, education, and research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".