When the Masks Come Off in Canada and Guatemala: Will the Realities of Racism and Marginalization of Midwives Finally Be Addressed?
Bibliographic record
Abstract
This article addresses the effects of COVID-19 in Eastern and Northern Ontario, Canada, with a comparative glimpse at the small province of Totonicapán, Guatemala, with which Canadians have been involved in obstetric and midwifery care in particular over the last 5 years. With universal health care coverage since 1966 and well-integrated midwifery, Canada's system would be considered relatively well set up to deal with a disaster like COVID-19 compared to low resource countries like Guatemala or countries without universal health care insurance (like the USA). However, the epidemic has uncovered the fact that in Ontario, Indigenous, Black, and People of Color (IBPOC), as elsewhere, may have been hardest hit, often not by actually contracting COVID-19, but by suffering secondary consequences. While COVID-19 could be an issue through which health care professionals can come together, there are signs that the medical hierarchies in many hospitals in both Ontario and Totonicapán are taking advantage of COVID-19 to increase interventive measures in childbirth and reduce midwives' involvement in hospitals. Meanwhile, home births are on the rise in both jurisdictions. Stories from a Jamaican Muslim woman in Ottawa, an Indigenous midwifery practice in Northern Ontario, registered midwives in Eastern Ontario, and about the traditional midwives in Guatemala reveal similar as well as unique problems resulting from the lockdowns. While this article is not intended to constitute an exhaustive analysis of social justice and human rights issues in Canada and Guatemala, we do take this opportunity to demonstrate where COVID-19 has become a catalyst that challenges the standard narrative, exposing the old ruts and blind spots of inequality and discrimination that our hierarchies and inadequate data collection-until the epidemic-were managing to ignore. As health advocates, we see signs that this pandemic is resulting in more open debate, which we hope will last long after it is over in both our countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".