Determinants of Children's Participation in California's Medicaid and SCHIP Programs
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To develop a comprehensive predictive model of eligible children's enrollment in California's Medicaid (Medi-Cal [MC]) and State Children's Health Insurance Program (SCHIP; Healthy Families [HF]) programs. DATA SOURCES/STUDY SETTING: 2001 California Health Interview Survey data, data on outstationed eligibility workers (OEWs), and administrative data from state agencies and local health insurance expansion programs for fiscal year 2000-2001. STUDY DESIGN: The study examined the effects of multiple family-level factors and contextual county-level factors on children's enrollment in Medicaid and SCHIP. DATA COLLECTION/EXTRACTION METHODS: Simple logistical regression analyses were conducted with sampling weights. Hierarchical logistic regressions were run to control for clustering. PRINCIPAL FINDINGS: Participation in MC and HF programs is determined by a combination of family-level predisposing, perceived need, and enabling/disabling factors, and county-level enabling/disabling factors. The strongest predictors of MC enrollment were family-level immigration status, ethnicity, and income, and the presence of a county-level "expansion program"; and the county-level ratio of OEWs to eligible children. Important HF enrollment predictors included family-level ethnicity, age, number of hours a parent worked, and urban residence; and county-level population size and outreach and media expenditure. CONCLUSIONS: MC and HF outreach/enrollment efforts should target poorer and immigrant families (especially Latinos), older children, and children living in larger and urban counties. To reach uninsured eligible children, it is important to further simplify the application process and fund selected outreach efforts. Local health insurance expansion programs increase children's enrollment in MC.
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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 | 0.003 | 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