Mapping Collaboration Across Birth Settings in the United States: Access, Equity, and Outcomes [2R]
Bibliographic record
Abstract
INTRODUCTION: Lack of coordination of care across birth settings (home-hospital, rural-urban) has been associated with adverse maternal-fetal outcomes. The US Birth Place Mapping Study examines associations between regulation, scope of practice, interprofessional collaboration with maternal-newborn outcomes, and equitable care for at-risk populations. METHODS: We populated a 50-state database with published regulatory data on scope of practice across birth settings. A nationwide survey of 92 regulatory experts verified the “on the ground” relevance, importance, and realities of collaboration. Content validation led to a 49-item weighted integration scoring system (item scores range 1-4). Composite summary scores were then used to rank states on the regulatory and practice environment for midwives and physicians across home, birth centers and hospitals. Higher scores indicate more integration and collaboration across all providers and all settings. Using CDC, Area Resource and CMS data, we calculated correlation coefficients between integration scores and selected outcomes (eg. SVD, VBAC, breastfeeding, cesarean, induction, neonatal mortality, LBW) in each state, controlling for race and type of provider. RESULTS: Integration scores ranged from 17-59, (North Carolina lowest – 17, Washington highest – 59). We report results through 4 interactive data maps, displaying quartiles for level of integration, linked to optimal and adverse outcomes, and access to care, by state. Rates of birth by race, Medicaid coverage, and location are displayed both by region and by individual states. CONCLUSION: This scoring system identifies barriers to collaboration in maternity care, and can inform health human resource planning and policy to improve regional access to high quality maternity 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".