Demographics of disenrollment from SCHIP: evidence from NJ KidCare
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 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