Inequities in Canadian maternal-child healthcare are perpetuating the intergenerational effects of colonization for indigenous women and children
Bibliographic record
Abstract
Universal, publicly funded healthcare has long been a point of pride for Canada, despite decades of data contradicting its universality and accessibility. Inequities in access to and provision of healthcare services are particularly evident in the direct comparison of health outcomes between Indigenous (First Nations, Inuit, and Métis) and non-Indigenous populations in Canada. Globally, there are data to support similar disparities in maternal-child health for Indigenous populations around the world. Here, we describe how these inequities uniquely impact people at the intersection of multiple vulnerabilities—Indigenous pregnant women and their children. Indigenous pregnant women in Canada are far more likely to have experienced harmful in utero exposures, inadequate antenatal care, and adverse birth outcomes than non-Indigenous pregnant women. These inequities in maternal-child health may be contributing to biological processes (e.g., epigenetic reprogramming) with intergenerational consequences for chronic disease risk in Indigenous populations. We highlight how the current state of maternal-child health for Indigenous women in Canada is likely perpetuating the multigenerational cycle of oppression triggered by the process of colonization. Finally, we outline current efforts to achieve reproductive justice, decolonize maternal-child health in Canada, and reclaim childbirth by Indigenous communities and their allies. We recognize the strength and resilience of Indigenous women in Canada to resist the persistence of colonial ideals in birthing rights and practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".