Recent Trends in Clinicians Providing Care to Pregnant Women in the United States
Bibliographic record
Abstract
INTRODUCTION: Health care needs of pregnant women are met by a variety of clinicians in a changing policy and practice environment. This study documents recent trends in types of clinicians providing care to pregnant women in the United States. METHODS: We used a repeat cross-sectional design and data from the Integrated Health Interview Series (2000-2009), a nationally representative data set, for respondents who reported being pregnant at the time of the survey (N = 3204). Using longitudinal logistic regression models, we analyzed changes over time in pregnant women's reported use of care from 1) obstetrician-gynecologists; 2) midwives, nurse practitioners (NPs), or physician assistants (PAs); or 3) both an obstetrician-gynecologist and a midwife, NP, or PA. RESULTS: The percentage of pregnant women who reported seeing an obstetrician-gynecologist (87%) remained steady from 2000 through 2009. After controlling for demographic and clinical variables, the percentage who reported receiving care from a midwife, NP, or PA increased 4% annually (yearly adjusted odds ratio [AOR] 1.04; P < .001), indicating a cumulative increase of 48% over the decade. The percentage of pregnant women who received care from both an obstetrician-gynecologist and a midwife, NP, or PA also increased (AOR 1.027; P < .001), for a cumulative increase of 30%. DISCUSSION: The increasing role of midwives, NPs, and PAs in the provision of maternity care suggests changes in the perinatal workforce and practice models that may promote collaborative care and quality improvement. However, better data collection is required to gather detailed information on specific provider types, these trends, and their implications.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".