Maternal health disparities: Challenges and recommendations to achieving equity and justice
Bibliographic record
Abstract
Maternal health disparities: Challenges and recommendations to achieving equity and justiceGlobally, the health of nations is often described in terms of public health statistical parameters, such as maternal morbidity, maternal morality (MMR) and infant mortality.Globally nations are compared by rank order using these indicators from lowest to high rates, and in terms of high-, middle-and low-income countries as well as world regions.Maternal morality describes the death of a woman during pregnancy or childbirth or within 42 days after the termination of pregnancy (World Health Organization, 2023a, 2023b).In 2020, the MMR was 223 per 100,000 live births, which far exceeds the United Nations Sustainable Development target of 70 deaths per 100,000 livebirths by 2030.Recent data show globally that maternal death rates are again raising, worldwide, after a period stagnant rate in 133 countries.There were significant increases in 17 countries, which included western Europe, North America, Latin America and the Caribbean (Khalil et al., 2023).Variations in MMR by region, often hide the mpact of maternal mortality.Global MMR for 2020 provide examples of variations among high income countries such as Norway (1.7), Australia (2.9), United Kingdom (9.6), Canada (11) and the United States (21.1).There are higher MMR among countries in regions such as Latin America-Mexico (59.1),South America-Peru (68.5),Brazil (72.1)Columbia (74.8),South Africa (126.8) and Sub-Sharan Africa (536; World Health Organization, 2023a, 2023b).Regardless to a country's level of health care systems, income status or geographic location, women who are Black, Indigenous or from People of Color (BIPOC) most often suffer the disparities of higher
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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 | 0.047 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.009 | 0.030 |
| Research integrity | 0.026 | 0.033 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".