A social law lens on maternal mortality in the U.S.: Lessons from Germany and the Netherlands
Bibliographic record
Abstract
Abstract The United States has one of the worst maternal mortality rates among developed nations. American mothers are three times more likely than Canadian mothers and six times more likely than Scandinavian mothers to die from pregnancy-related deaths. Currently, for every 100,000 live births, 26.4 mothers are dying in the U.S, with significant disparities between White mothers and mothers of color. Projections indicate that by 2030, the maternal mortality rate will rise to 45 maternal deaths out of 100,000 live births. In direct contrast, most other similarly situated high-income nations have decreased their maternal mortality rates in recent years, evidencing only single-digit mortality per 100,000 mothers. This research examines how social protection measures afforded by the law can facilitate differences in these rates. Specifically, this presentation compares legal interventions enshrined in social law that impact maternal health in the United States, Germany, and the Netherlands, including mandated access to prenatal care, midwifery reimbursement, and obligatory duration of postnatal care. Compared to the United States, both Germany and the Netherlands enshrine more comprehensively midwifery compensation and access to postnatal care in their social legal codes and insurance benefit schemes. Evidence accumulated by comparing these interventions with maternal mortality statistics suggests that legal interventions that spur extra attention to mothers during and after birth may help prevent pregnancy-related deaths. It also opens a discussion about how policymakers can use legal interventions to help eliminate racial disparities in maternity practice. Key messages Codified legal interventions that mandate extra attention to mothers during and after birth may help prevent pregnancy-related deaths. Compared to the U.S., both Germany and the Netherlands better enshrine midwifery compensation and access to postnatal care in their social legal codes and insurance benefit schemes.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".