Indigenous Peoples’ evaluation of health risks when facing mandatory evacuation for birth during the COVID-19 pandemic: An Indigenous feminist analysis
Bibliographic record
Abstract
Abstract Background Indigenous Peoples living on Turtle Island are comprised of First Nations, Inuit, and Métis people and because of the Government of Canada’s mandatory evacuation policy, those living in rural and remote regions of Ontario are required to travel to urban, tertiary care centres to give birth. When evaluating the risk of travelling for birth, Indigenous Peoples understand, evaluate, and conceptualise health risks differently than Eurocentric biomedical models of health. Also, the global COVID-19 pandemic changed how people perceived risks to their health. Our research goal was to better understand how Indigenous parturients living in rural and remote communities conceptualised the risks associated with evacuation for birth before and during the COVID-19 pandemic. Methods To achieve this goal, we conducted semi-structured interviews with 11 parturients who travelled for birth during the pandemic and with 5 family members of those who were evacuated for birth. Results Participants conceptualised evacuation for birth as riskier during the COVID-19 pandemic and identified how the pandemic exacerbated existing risks of travelling for birth. In fact, Indigenous parturients noted the increased risk of contracting COVID-19 when travelling to urban centres for perinatal care, the impact of public health restrictions on increased isolation from family and community, the emotional impact of fear during the pandemic, and the decreased availability of quality healthcare. Conclusions Using Indigenous Feminist Methodology and Indigenous Feminist Theory, we critically analysed how mandatory evacuation for birth functions as a colonial tool and how conceptualizations of risk empowered Indigenous Peoples to make decisions that reduced risks to their health during the pandemic. With the results of this study, policy makers and governments can better understand how Indigenous Peoples conceptualise risk related to evacuation for birth before and during the pandemic, and prioritise further consultation with Indigenous Peoples to collaborate in the delivery of the health and care they need and desire.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".