Addressing Black Maternal Mortality
Bibliographic record
Abstract
Increasing rates of maternal morbidity and mortality is a growing concern in many industrialized countries. Data from US maternal mortality review committees indicate that more than 80% of these deaths are preventable (Trost et al., 2019). Various factors contributing to this issue include advancing maternal age, increased adults living with congenital disease that may affect outcomes and increased prevalence of comorbidities such as diabetes and hypertension (Fink et al., 2023). In the United States, black women are disproportionately affected by maternal mortality and severe maternal morbidity, facing rates almost three times higher than those of white women (Hoyert, 2023). Few Canadian studies exist, but they echo similar findings. Research by McKinnon and colleagues (2016) found that Black women were more likely to have premature babies and have their pain experiences discounted. A more recent qualitative study based in Toronto reported pervasive obstetric racism experienced by Black women (Boakye et al., 2023). These disparities stem from a complex interplay of factors, including systemic racism, socio-economic disparities, and unequal access to quality healthcare. Unfortunately, research on Black maternal mortality and morbidity is limited in Canada, and we lack a much-needed national system to track these outcomes. Unlike the United States, few Canadian health agencies collect racial statistics. However, disaggregated race-based data is critical for informing targeted interventions and policy changes. This infographic was created under the umbrella of The Newcomer Health Hub, a Canadian medical student-run organization that seeks to increase awareness of health-care disparities in order to improve medical training. Infographics play a crucial role in enhancing comprehension, increasing engagement, and promoting health literacy. Together, through education, advocacy, and community engagement, we can work towards ensuring that every mother receives the support, resources, and care they need to have a safe and healthy pregnancy and childbirth experience, regardless of race or ethnicity.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".