End-of-life issues for aboriginal patients: a literature review.
Bibliographic record
Abstract
OBJECTIVE: To understand some of the cross-cultural issues in providing palliative care to aboriginal patients. SOURCES OF INFORMATION: MEDLINE (1966 to 2005), CINAHL, PsycINFO, Google Scholar, and the Aboriginal Health Collection at the University of Manitoba were searched. Studies were selected based on their focus on both general cross-cultural caregiving and, in particular, end-of-life decision making and treatment. Only 39 relevant articles were found, half of which were opinion pieces by experienced nonaboriginal professionals; 14 were qualitative research projects from nursing and anthropologic perspectives. MAIN MESSAGE: All patients are unique. Some cultural differences might arise when providing palliative care to aboriginal patients, who value individual respect along with family and community. Involvement of family and community members in decision making around end-of-life issues is common. Aboriginal cultures often have different approaches to telling bad news and maintaining hope for patients. Use of interpreters and various communication styles add to the challenge. CONCLUSION: Cultural differences exist between medical caregivers and aboriginal patients. These include different assumptions and expectations about how communication should occur, who should be involved, and the pace of decision making. Aboriginal patients might value indirect communication, use of silence, and sharing information and decision making with family and community members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".