Women In The United States Experience High Rates Of Coverage ‘Churn’ In Months Before And After Childbirth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it