The impact of family physicians in rural maternity care
Bibliographic record
Abstract
BACKGROUND: Reduced access to maternity care in rural areas of the United States presents a significant burden to pregnant persons and infants. The objective of this study was to estimate the impact of family physicians (FPs) on access to maternity care in rural United States hospitals, especially where other providers may not be available. METHODS: We administered a survey to 216 rural hospitals in 10 US states inquiring about the number of babies delivered from 2013 to 2017, the types of delivering physicians, and the maternity services offered. We calculated the percentage of rural hospitals in our sample where FPs performed vaginal deliveries, cesareans, and vaginal births after cesarean (VBACs), and the percentage of all babies delivered by FPs. We estimated the distance patients would have to travel for care if FPs were not providing care locally. RESULTS: The final study population consisted of 185 rural hospitals. FPs delivered babies in 67% of these hospitals and were the only physicians who delivered babies in 27% of these hospitals. FPs provided VBAC at 18% and cesarean birth services at 46% of the rural hospitals, but with wide geographic differences. Many patients would have to drive an average of 86 miles round-trip to access care if those FPs were to stop delivering. CONCLUSIONS: Family physicians are essential providers of maternity care in the rural United States. Family Medicine residency programs should ensure that trainees who intend to practice in rural locations have adequate maternity care training to maintain and expand access to maternity care for rural patients and their families.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".