Indigenous-specific cultural safety within health and dementia care: A scoping review of reviews
Bibliographic record
Abstract
Globally, health inequities experienced by Indigenous communities are often described and documented in terms of deficits and disease. However, health disparities are complex and involve numerous underlying issues beyond the social determinants of health. Indigenous Peoples face unique barriers to accessing culturally safe and equitable healthcare, including racism, systemic injustice, and a historical legacy of colonialism. There is a paucity of knowledge on Indigenous-specific cultural safety interventions to support health and dementia care. The objective of this scoping review of reviews was to appraise the existing literature to identify key elements, conceptualizations, and interventions of cultural safety to improve health services and dementia care for Indigenous Peoples. Guided by Indigenous principles of relationality, we conducted a scoping review of reviews published between January 2010 to December 2020. We searched CINAHL, PubMed, Scopus, Web of Science, and Google Scholar. Given the limited literature focusing specifically on Indigenous people with dementia, our inclusion criteria focused broadly on Indigenous cultural safety in healthcare. A collaborative and relational rights-based approach co-led by Indigenous cisgender, Two-Spirit, and non-Indigenous cisgender health care providers was used to re-center Indigenous ways of knowing. A total of seventeen articles met our inclusion criteria. Our review identified a range of cultural safety themes from education initiatives to collaborative partnerships with Indigenous communities. Themes emerged at three levels: person-centered/individual level, health practitioner/student level, and healthcare organizational level. Few reviews described specific interventions, implementation strategies, evaluation methods, or the concept of sex and gender to improve cultural safety in healthcare delivery. Findings from this review can help to inform future research, inspire innovative collaborative methodologies, and enhance cultural safety interventions. In moving forward, there is an urgent need for anti-racism education, self-determination, and authentic partnerships to achieve Indigenous-specific cultural safety inclusive of sex and gender considerations in health and dementia care.
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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) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".