Cultural safety strategies for rural Indigenous palliative care: a scoping review
Bibliographic record
Abstract
BACKGROUND: There is little scholarship on culturally safe approaches to palliative care, especially for rural Indigenous clients. Thus, it is important to articulate how cultural safety can be enacted to support rural Indigenous Peoples and communities at end of life. We sought to identify strategies described in existing literature that have potential to deepen our understanding of culturally safe approaches to palliative care within rural and small-town settings in Canada. METHODS: We searched for peer-reviewed and grey literature about Indigenous palliative care in rural and small-town settings in Canada, United States, New Zealand, and Australia. Medline, CINAHL, and Embase were searched. We thematically analyzed 22 resulting articles to address our interest in culturally safe approaches to palliative care in rural/small-town and on-reserve contexts. RESULTS: The following themes were extracted from the literature: symbolic or small gestures; anticipating barriers to care; defer to client, family and community; shared decision-Making; active patient and family involvement; respectful, clear, and culturally appropriate communication; community ownership of services; empower cultural identity, knowledge, and traditions; and, policy. DISCUSSION: Culturally competent practices can improve Indigenous palliative care services; however, they do not result in decolonized care. Strategies include: symbolic or small gestures; anticipating barriers to access; deferring to the client, family, and community members; and, collective decision making and family involvement. Culturally safe approaches contribute to institutional or organizational change and decolonized care. Strategies include: involvement of patient and family in service planning; reflection about individual and systemic racism; community ownership of services and; recognizing distinct Worldviews that shape care. CONCLUSIONS: Culturally safe strategies invite decolonization of care through awareness of colonialism, racism, and discrimination. They invite commitment to building partnerships, power sharing, and decision-making in the delivery of care. Culturally competent activities may catalyze the adoption of a cultural safety framework; however, mislabeling of cultural competency as cultural safety may contribute to organizational inaction and a watering down of the spirit of cultural safety.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".