Indigenous Peoples’ responses to evacuation for birth in Ontario: conceptualizing risk through an Indigenous midwifery-led approach
Bibliographic record
Abstract
BACKGROUND: Currently, pregnant Indigenous Peoples living in remote, rural, and northern Indigenous communities in Canada are subjected to evacuation birth policy, whereby they are evacuated out of their community to large, urban hospitals to give birth. Evacuation for birth is assumed to decrease biomedical risk because people are birthing in hospitals. In Canadian health systems, evaluating and mitigating biomedical risk has become a standard in health decision-making but this framework disregards Indigenous ontologies and epistemologies that guide Indigenous people in their evaluation of health risk. In this study, we sought to understand how pregnant Indigenous people in Ontario conceptualise health and risk. METHODS: We collected data through semi-structured interviews with 43 participants who have been evacuated for birth or are kin of an evacuee who live in Ontario, Canada. RESULTS: Risks associated with evacuation for birth were conceptualised by participants in a wholistic manner based on principles of self-determination. Participants identified multiple risks that shaped their overall assessment of health risk when facing evacuation for birth including the risk of being separated from kin, confronting a lack of health services, and experiencing discrimination. As participants spoke about risk, they reimagined perinatal care to mitigate these risks, which requires bringing birth back to Indigenous communities through Indigenous midwifery. CONCLUSIONS: We outline actions to limit the practice of evacuation for birth, support the return of birth to Indigenous communities, and expand understandings of risk within policy and clinical practice.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".