Demographics of disenrollment from SCHIP: evidence from NJ KidCare
Bibliographic record
Abstract
The State Children's Health Insurance Program (SCHIP) provides health insurance coverage for children in low-income families. Although there is evidence of substantial disenrollment from SCHIP, few studies have examined how disenrollment varies by demographic characteristics. This study uses data from administrative records of all 41,881 children enrolled prior to April 2000 in NJ KidCare (New Jersey's SCHIP) separate state plans for families with incomes between 133% and 350% of the Federal Poverty Level. Survival methods were used to analyze disenrollment according to demographic and plan characteristics. Reasons for disenrollment were also studied. Overall, 18.9% of children disenrolled within 12 months of enrollment. Disenrollment was higher among non-Hispanic black children, children aged 1 to 5, and children without siblings in NJ KidCare than among their counterparts. Surprisingly, English speakers had the highest disenrollment rate of all language groups. Children in families with moderate income categories for whom premium contributions were required were 3 times as likely as lower-income children to disenroll, principally due to non-payment of premiums. To maximize retention in SCHIP and ensure access to care and continuity of care for low-income children, research is needed concerning why some groups disenroll more quickly.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".