Maternal Mortality, United States and Canada, 1982–1997
Bibliographic record
Abstract
BACKGROUND: The 1998 public awareness campaign on Safe Motherhood called attention to the issue of maternal mortality worldwide. This paper focuses upon maternal mortality trends in the United States and Canada, and examines differentials in maternal mortality in the United States by maternal characteristics. METHODS: Data from the vital statistics systems of the United States and Canada were used in the analysis. Both systems identify maternal deaths using the definition of the World Health Organization's International Classification of Diseases. Numbers of deaths, maternal mortality rates, and confidence intervals for the rates are shown in the paper. RESULTS: Maternal mortality declined for much of the century in both countries, but the rates have not changed substantially between 1982 and 1997. In this period the maternal mortality levels were lower in Canada than in the United States. Maternal mortality rates vary by maternal characteristics, especially maternal age and race. CONCLUSIONS: Maternal mortality continues to be an issue in developed countries, such as the United States and Canada. Maternal mortality rates have been stable recently, despite evidence that many maternal deaths continue to be preventable. Additional investment is needed to realize further improvements in maternal mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".