Determinants of Children's Participation in California's Medicaid and SCHIP Programs
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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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 | 0.001 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".