Colonial Legacies and Collaborative Action: Improving Indigenous Peoples’ Health Care in Canada
Bibliographic record
Abstract
Indigenous people experience significant health disparities compared to non-Indigenous people, which are exacerbated by less accessible and poorer quality health care services. This research aimed to understand the specific barriers to health care that Indigenous patients and their families face, as well as to explore promising practices and strategies for improving the responsiveness of health services to the needs of Indigenous people. Through qualitative interviews with Indigenous and non-Indigenous health care and social services providers, we identified a range of challenges and successful approaches, and developed recommendations for improving policy and practice to address the gaps in culturally safe health care services. Our study shows that many of the barriers Indigenous people face when accessing health care are rooted in the broader social determinants of health, such as poverty, racism, housing, and education. These are complex problems that are outside of the traditional scope of health care practice. However, this study has also demonstrated that many barriers to equitable care actually stem from within the health care system itself. We found that health care gaps were often attributable to poorly funded on-reserve health care services and culturally unsafe off-reserve services. Attitudes and practices among those working in health care and gaps in coordination between mainstream and Indigenous services are challenges related to the way the health care system operates. Solutions are needed that address these issues. Given the multifaceted nature of access barriers, strategies to improve health services for Indigenous people and communities require a comprehensive and systemic approach.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 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".