Cultural safe healthcare initiatives, implementations, and recommendations, for Indigenous peoples of Canada: a systematic review
Bibliographic record
Abstract
The health of Indigenous peoples across Canada continues to be significantly impacted by experiences of racism when seeking healthcare. Implementing cultural safety was identified by the Truth and Reconciliation Commission of Canada as a critical way to mitigate these negative health consequences. This systematic review aims to outline cultural safety and its associated derivatives in academia, what cultural safety is, and why it is important in the context of indigenous healthcare. A systematic search of PubMed was carried out refining searches to Canadian contexts, published after December of 2015, and limited to peer-reviewed reviews and systematic reviews. A thematic review of the articles identified four central ideas of importance regarding the information presented in the papers; definitions of cultural safety and associated derivatives, the importance of including culture in healthcare, recommendations to healthcare settings, and evaluation methods of cultural safety initiatives. It is clear that there is a need for an explicit and consistent definition of cultural safety with the inclusion of Indigenous peoples in the creation of this definition. To determine effectiveness, gaps and areas for improvement, evaluation methods inclusive of the unique Indigenous worldviews are imperative to develop culturally safe healthcare practices and institutions.
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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".