The role of midwifery and other international insights for maternity care in the United States: An analysis of four countries
Bibliographic record
Abstract
BACKGROUND: The United States (US) spends more on health care than any other high-resource country. Despite this, their maternal and newborn outcomes are worse than all other countries with similar levels of economic development. Our purpose was to describe maternal and newborn outcomes and organization of care in four high-resource countries (Australia, Canada, the Netherlands, and United Kingdom) with consistently better outcomes and lower health care costs, and to identify opportunities for emulation and improvement in the United States. METHOD: We examined resources that described health care organization and financing, provider types, birth settings, national, clinical guidelines, health care policies, surveillance data, and information for consumers. We conducted interviews with country stakeholders representing the disciplines of obstetrics, midwifery, pediatrics, neonatology, epidemiology, sociology, political science, public health, and health services. The results of the analysis were compared and contrasted with the US maternity system. RESULTS: The four countries had lower rates of maternal mortality, low birthweight, and newborn and infant death than the United States. Five commonalities were identified as follows: (1) affordable/ accessible health care, (2) a maternity workforce that emphasized midwifery care and interprofessional collaboration, (3) respectful care and maternal autonomy, (4) evidence-based guidelines on place of birth, and (5) national data collections systems. CONCLUSIONS: The findings reveal marked differences in the other countries compared to the United States. It is critical to consider the evidence for improved maternal and newborn outcomes with different models of care and to examine US cultural and structural failures that are leading to unacceptable and substandard maternal and infant 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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".