Maternal and neonatal health in Canada’s Black communities: A scoping review of epidemiologic studies
Bibliographic record
Abstract
OBJECTIVES: Black-White disparities in maternal and neonatal morbidity and mortality highlight health inequities in several settings, yet such racial disparities in Canada are not well defined. Our objective was to conduct a scoping review to identify the extent of epidemiologic evidence assessing Black-White disparities in maternal and neonatal health in Canada. METHODS: We included peer-reviewed epidemiologic studies which measured maternal or neonatal outcomes in Black versus White individuals in Canada. We searched OVID platforms (MEDLINE, Embase, Emcare) from inception to May 9, 2024, using keywords and controlled vocabulary terms related to race and maternal and neonatal morbidity and mortality. Results synthesis was carried out using descriptive analysis. SYNTHESIS: After exclusions, six retrospective cohort studies were included in the scoping review. The majority of the included studies used data obtained from provincial datasets (n = 5), defined maternal race using self-reported race (n = 5), and were set in Ontario (n = 4). All studies reported one or more significant associations between race and adverse maternal or neonatal outcomes, with Black individuals experiencing higher rates of spontaneous fetal loss (n = 1), perinatal mortality (n = 1), preterm birth (n = 3), small for gestational age infants (n = 1), low Apgar scores (n = 2), congenital heart disease (n = 1), neonatal intensive care unit admission (n = 1), preeclampsia (n = 2), gestational diabetes (n = 1), and inadequate gestational weight gain (n = 1). CONCLUSION: Although literature on the topic is sparse, Black-White disparities in maternal and neonatal health in Canada are apparent. National, population-based data are needed to provide a comprehensive understanding of racial disparities in maternal and neonatal health and the factors driving these differences.
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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 | 0.024 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.030 | 0.045 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".