Understanding the Influence of Culture on End-of-Life, Palliative, and Hospice Care: A Narrative Review
Bibliographic record
Abstract
Cultural beliefs and values significantly shape end-of-life care decisions, including palliative and hospice care approaches. These cultural influences affect how patients and families view death, pain management, and medical decision-making. This narrative review examines how cultural factors impact care delivery for patients with terminal diagnoses, with particular attention to improving patient-provider communication and trust across diverse populations. A search of the current literature was conducted from March 1, 2022, to May 1, 2022, through the following databases: PubMed, ScienceDirect, Google Scholar, Directory of Open Access Journals, JSTOR, PsycINFO, ERIC Database (via EBSCOhost), and Academic Search Complete. The following key terms were used with Boolean operations: culture, demographic, end-of-life care, hospice, palliative care, terminal illness, and truth disclosure. A combination of qualitative, quantitative, mixed methods, and review studies was included in the review. The Mixed Methods Appraisal Tool was used to appraise quantitative and qualitative literature critically, and the Risk of Bias in Systematic Review was used to appraise reviews critically. This narrative review included 25 relevant publications related to influence of culture and patient demographics on end-of-life care, hospice, and palliative care. As each culture has its own unique views on death and dying, it is crucial to note these cultural differences when assisting with end-of-life care to best align with patients' beliefs and values. Themes such as cultural barriers, communication preferences and family roles emerged from the publications. The findings of this review highlight the critical role that cultural beliefs and values play in shaping end-of-life care experiences. However, gaps remain in the literature regarding how specific cultural nuances influence care decisions, communication preferences, and family dynamics. Future research should focus on exploring underrepresented ethnic and cultural groups to better understand their unique perspectives on death, dying, and medical decision-making. Additionally, there is a need to develop and evaluate culturally tailored interventions that promote patient-centered care, enhance provider-patient communication, and build trust in end-of-life care settings. Addressing these gaps will help ensure that care is respectful of and responsive to the diverse cultural contexts in which patients and families make these deeply personal decisions.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| 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".