Barriers and Mitigating Strategies to Healthcare Access in Indigenous Communities of Canada: A Narrative Review
Bibliographic record
Abstract
The objective of this review is to document contemporary barriers to accessing healthcare faced by Indigenous people of Canada and approaches taken to mitigate these concerns. A narrative review of the literature was conducted. Barriers to healthcare access and mitigating strategies were aligned into three categories: proximal, intermediate, and distal barriers. Proximal barriers include geography, education attainment, and negative bias among healthcare professionals resulting in a lack of or inadequate immediate care in Indigenous communities. Intermediate barriers comprise of employment and income inequities and health education systems that are not accessible to Indigenous people. Distal barriers include colonialism, racism and social exclusion, resulting in limited involvement of Indigenous people in policy making and planning to address community healthcare needs. Several mitigation strategies initiated across Canada to address the inequitable health concerns include allocation of financial support for infrastructure development in Indigenous communities, increases in Indigenous education and employment, development of culturally sensitive education and medical systems and involvement of Indigenous communities and elders in the policy-making system. Indigenous people in Canada face systemic/policy barriers to equitable healthcare access. Addressing these barriers by strengthening services and building capacity within communities while integrating input from Indigenous communities is essential to improve accessibility.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".