The State Children’s Health Insurance Program: A Multicenter Trial of Outreach Through the Emergency Department
Bibliographic record
Abstract
OBJECTIVES: We evaluated emergency department (ED)-based outreach for the State Children's Health Insurance Program (SCHIP). METHODS: We conducted a multicenter trial among uninsured children (< or = 18 years) who presented to 5 EDs in 2001 and 2002. On-site staff enrolled consecutive subjects for a control period followed by an intervention period during which staff handed out SCHIP applications to the uninsured. The primary outcome was state-level confirmation of insured status at 90 days. RESULTS: We followed 223 subjects (108 control, 115 intervention) by both phone interview and state records. Compared to control subjects, those receiving a SCHIP application were more likely to have state health insurance at 90 days (42% vs 28%; P<.05; odds ratio [OR]=3.8; 95% confidence interval [CI]=1.7, 8.6). Although the intervention effect was prominent among 118 African Americans (50% insured after intervention vs 31% of controls, P<.05), lack of family enrollment in other public assistance programs was the primary predictor of intervention success (OR=3.7; 95% CI=1.6, 8.4). CONCLUSIONS: Handing out insurance applications in the ED can be an effective SCHIP enrollment strategy, particularly among minority children without connections to the social welfare system. Adopted nationwide, this simple strategy could initiate insurance coverage for more than a quarter million additional children each year.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".