Anti-Black Racism and Maternal Health in the Greater Toronto Area
Bibliographic record
Abstract
In June 2020, the Toronto Board of Public Health declared anti-Black racism a public health crisis. Yet, race-based data with respect to the maternal healthcare experiences of Black women in Canada is limited (Turner et al., 2020). Recognizing perinatal care as a site of racialization (Bridges, 2011), this study uses Critical Race Theory (Crenshaw, 2011) and Reproductive Justice Framework (Ross & Solinger, 2017) to investigate the relationship between race and perinatal experiences of Black women. This research conducted a document analysis of postpartum material (Weber & Hilfinger Messias, 2012) and collected semi-structured interview data from 7 Black women (Hayes & Casstevens, 2017) who delivered a child in January 2020 to May 2023 and sought care in a hospital in the Greater Toronto Area. It is evident that approaches for person-centred care are needed in the Canadian health care system, as it is critical in providing Black women with dignified care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".