CHIP-ing Away at Health Disparities: Has State-Provided Health Insurance Reduced Race- and Nativity-Based Differences in Health Care Utilization among US Children?
Bibliographic record
Abstract
Although recent legal permanent residents and undocumented immigrants are generally barred from accessing public health insurance, some US states cover immigrant children through the Children’s Health Insurance Program (CHIP). In this study, we examine the contextual effect of US state health insurance eligibility policy, particularly with respect to immigrant children, on race/ethnic and nativity-based disparities in children’s routine health care. Utilizing our original data on state CHIP eligibility policies and child-level data from the Survey of Income and Program Participation, we find that a significant portion of between-state variation in children’s routine health care results from diversity in CHIP eligibility rules for poor and foreign-born children. Immigrant-specific disparities are reduced when states do not require five years of residency for CHIP participation.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".