Cultural Influences, Considerations, and Challenges for Organ Donation and Liver Transplantation Around the World
Bibliographic record
Abstract
INTRODUCTION: Although liver transplantation (LT) rates have increased globally, the demand for organs still exceeds the supply. This review highlights cultures with significant influences on organ donation and LT. METHODS: We performed a literature search for articles related to cultures, ethnologies, philosophies, policies, religions, socio-economic factors, and other influences affecting organ donation in LT around the world. RESULTS: There are astonishing similarities between different cultures. In Africa, Asia, and Europe, people often view the postmortem bodies as sacred and believe that their bodies are gifts from "God" or ancestors. To respect the gift of life, to have a chance in reincarnation, or to enjoy life after death, many believe that their bodily sanctity must be preserved. In Asia, Europe, and Latin America, there is a general reluctance to speak about death. Religions also play an important role in people's views on organ donation. Different regions around the world have similar challenges to organ donation, such as misconceptions about organ trafficking, political and economic instability, and a lack of governmental or religious support. CONCLUSION: Addressing the complexities of organ donation and LT requires a multifaceted approach. Culturally sensitive education tailored to diverse communities can dispel myths and misconceptions surrounding organ donation. Fostering partnerships between healthcare providers, religious leaders, community organizations, and policymakers can further facilitate open dialogues and promote organ donation as a noble act to save lives. SOCIAL MEDIA: Similar cultures around the world post challenges for organ donation. Organizations should foster partnerships between healthcare providers, politicians, and religious leaders to promote donation.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".