Measuring the Impact of Outreach and Enrollment Strategies for Public Health Insurance in California
Bibliographic record
Abstract
UNLABELLED: OBJECTIVE AND STUDY SETTING: To evaluate the effectiveness of different approaches to outreach on public health insurance enrollment in 25 California counties with a Children's Health Initiative. DATA SOURCE: Administrative enrollment databases. STUDY DESIGN: The use of eight enrollment strategies were identified in each quarter from 2001 to 2007 for each of 25 counties (county quarter). Strategies were categorized as either technology or nontechnology. New enrollments were obtained for Medi-Cal, Healthy Families, and Healthy Kids. Bivariate and multivariate analyses assessed the link between each strategy and new enrollments rates of children. DATA COLLECTION: Methods Surveys of key informants determined whether a specific outreach strategy was used in each quarter. These were linked to new enrollments in each county quarter. PRINCIPAL FINDINGS: Between 2001 and 2007, enrollment grew in all three children's health programs. We controlled for the effects of counties, seasons, and county-specific child poverty rates. There was an increase in enrollment rates of 11 percent in periods when technology-based systems were in use compared with when these approaches were inactive. Non-technology-based approaches, including school-linked approaches, yielded a 12 percent increase in new enrollments rates. Deploying seven to eight strategies yielded 54 percent more new enrollments per 10,000 children compared with periods with none of the specific strategies. CONCLUSIONS AND IMPLICATIONS: National health care reform provides new opportunities to expand coverage to millions of Americans. An investment in technology-based enrollment systems will maximize new enrollments, particularly into Medicaid; nontechnological approaches may help identify harder-to-reach populations. Moreover, incorporating several strategies, whether phased in or implemented simultaneously, will enhance enrollments.
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How this classification was reachedexpand
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 | 0.009 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".