Canada's pregnancy-related mortality rates: doing well but room for improvement
Bibliographic record
Abstract
PURPOSE: Canada's perinatal, infant and maternal mortality rates were examined and compared with other Organization for Economic Cooperation and Development (OECD) countries. The type and the quality of the available data and best practices in several OECD countries were evaluated. SOURCE: A literature search was performed in PubMed and the Cochrane Library. Vital statistics data were obtained from the OECD Health Database and Statistics Canada and subjected to secondary analysis. PRINCIPAL FINDINGS: Overall, Canadian pregnancy mortality rates have fallen dramatically since the early 1960's. Perinatal and infant mortality rates remain low and stable, but the maternal mortality rate has increased slightly and both mortality rates have declined in their relative OECD rankings over the last 20 years. Data quality and coverage across Canada and internationally, especially for Indigenous peoples, is inconsistent and registration practices differ greatly, making comparisons difficult. Available data do show that Indigenous people's perinatal and infant mortality rates are nearly twice those of the general population. Best practices in other OECD countries include Australia's National Maternity Services plan to improve Aboriginal perinatal health, the Netherlands' midwifery services and National Perinatal Registry and Japan's national pregnancy registration and Maternal Handbook. CONCLUSION: To diminish Canadian disparities in perinatal health rates and improve health outcomes we recommend a) uniform registration practices across Canada, b) better data quality and coverage especially among Indigenous communities, c) adoption of a national pregnancy registration and a maternal handbook along with d) improved midwifery and primary practice services to rural and remote communities. At a time when Canada is focusing upon improving pregnancy health in developing nations, it also needs to address its own challenges in improving pregnancy outcomes.
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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 | 0.031 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.028 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".