Barriers to SCHIP enrollment
Bibliographic record
Abstract
BACKGROUND. Enrollment in the State Children's Health Insurance Program (SCHIP), created under the federal Balanced Budget Act of 1997, had a distressingly slow start and varied substantially county-to-county in many states, including Pennsylvania. METHODS. We performed a quantitative county-level analysis of barriers to enrollment in Pennsylvania's Children's Health Insurance Program (CHIP) for the year 2000, seven years after it was implemented and three years after federal SCHIP legislation. Using multivariate regression analysis with a county as the unit of observation, we modeled enrollment in SCHIP as a function of accessibility to health care, availability of clinicians, and community economic health. RESULTS. High clinic density and Medicaid managed-care membership predicted SCHIP enrollment success, while female head-of-household predicted SCHIP enrollment failure. A principal-components factor analysis revealed four underlying barriers to enrollment: accessibility, availability, affordability, and effort. CONCLUSIONS. The most formidable barriers to SCHIP enrollment success in Pennsylvania were not programmatic; they were correlates of poverty itself.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".