Women In The United States Experience High Rates Of Coverage ‘Churn’ In Months Before And After Childbirth
Bibliographic record
Abstract
Insurance transitions-sometimes referred to as "churn"-before and after childbirth can adversely affect the continuity and quality of care. Yet little is known about coverage patterns and changes for women giving birth in the United States. Using nationally representative survey data for the period 2005-13, we found high rates of insurance transitions before and after delivery. Half of women who were uninsured nine months before delivery had acquired Medicaid or CHIP coverage by the month of delivery, but 55 percent of women with that coverage at delivery experienced a coverage gap in the ensuing six months. Risk factors associated with insurance loss after delivery include not speaking English at home, being unmarried, having Medicaid or CHIP coverage at delivery, living in the South, and having a family income of 100-185 percent of the poverty level. To minimize the adverse effects of coverage disruptions, states should consider policies that promote the continuity of coverage for childbearing women, particularly those with pregnancy-related Medicaid eligibility.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".