A Review of Maternal and Child Health Status in Canada and Japan – Reinvigorating Maternal and Child Health Handbook
Bibliographic record
Abstract
Objective: The objective of this study is to identify the challenges in Canadian health-care system that contributes to higher perinatal, infant, and maternal mortality rate (MMR) compared to other Organization for Economic Co-Operation and Development (OECD) countries, especially Japan, and to make a recommendation based on available data.Materials and Methods: Systematic literature search was performed through PubMed, Cochrane Library, and University of Toronto online library.Available data on maternal and child health (MCH) were collected from relevant literature, Statistics Canada and OECD database for secondary analysis.Results: We identified that, throughout the period from 2006 to 2016, Canada had a higher rate of maternal mortality compared to Japan, especially in 2014, when MMR in Canada (6.0) was almost twice the rate of Japan (3.3).Between 2005 and 2018, there was a gradual decline in infant mortality rate for both countries, but Japan performed well in keeping the infant mortality rate significantly lower than Canada (1.9 for Japan and 4.5 for Canada in 2017).Introduction of MCH Handbook contributed to keeping up the national health indicators high in Japan even before Japan attained a stable economy.However, differences in national registration practice among OECD countries and inconsistencies in data coverage across Canada caused difficulty in making comparisons.Conclusion: Nationally standardized process is needed to investigate and document maternal deaths, which will help Canada take concrete actions to make pregnancy safer for women.Effective partnerships among government agencies, health-care institutions, and pregnant women and their families are needed to ensure the best 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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.033 | 0.039 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".