Cultural Safety within the Indigenous Health Context: Findings from a Review of Reviews
Bibliographic record
Abstract
Abstract First Nations, Inuit, and Métis older adults often face systemic barriers to accessing culturally safe and equitable healthcare, including racism, structural injustice, and a historical legacy of colonialism. However, there is a paucity of knowledge on cultural safety interventions and implementation strategies in care for older adults. This presentation aims to: 1) explore persistent barriers to achieving health equity and advancing cultural safety in healthcare; and 2) identify cultural safety interventions to improve healthcare for Indigenous older adults. Guided by Arksey and O’Malley’s scoping review framework, we conducted a review of reviews published between January 2010 to December 2020 on Indigenous cultural safety in healthcare. We searched five databases (CINAHL, PubMed, Scopus, Web of Science, and Google Scholar) and hand-searched reference lists of relevant articles. We conducted a thematic analysis to identify patterns and themes in the literature. Key barriers to achieving health equity and advancing cultural safety in healthcare included care providers lacking knowledge of Indigenous culture, power imbalances, racism, and discrimination. A range of cultural safety interventions were identified, from education and training initiatives for healthcare providers (emergency physicians and occupational therapists) to collaborative partnerships with First Nations, Inuit, and Métis communities. As First Nations, Inuit, and Métis populations age, there is a growing need for safe healthcare services for Indigenous older adults, and these findings suggest focusing on healthcare providers knowledge and attitudes is key. Research is necessary to develop, implement, and evaluate cultural safety interventions aimed at healthcare providers to improve healthcare for Indigenous older adults.
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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 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".