Hua Wang, 12/6/2004 The Effects of the State Children’s Health Insurance Program (SCHIP) on Children’s Health Insurance Coverage
Bibliographic record
Abstract
This paper analyzes the effects of availability of the State Children’s Health Insurance Program (SCHIP) on children’s SCHIP enrollment and Medicaid enrollment in addition to more commonly focused overall public and private coverage and the uninsured rate. Linear probability models with controls for state and quarter of year fixed effects were estimated using data from the National Health Interview Survey (NHIS) for the years 1995 through 2002. My results show that nationwide among children for whom SCHIP became available, 5 percent enrolled in SCHIP, less than 3 percent gained public coverage, 1.4 percent lost private insurance, and 1.1 percent became no longer uninsured. The spillover effect of SCHIP implementation on Medicaid enrollment among children under age 6 and with family income below poverty level was 5.5 percent. SCHIP’s effects on health coverage were greater for lower-income children, older children, and states implementing only stand-along programs. These findings, many of which are similar to other recent national estimates, will contribute to the upcoming Congressional discussion about the reauthorization of SCHIP. 1.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".