Improvements in Access to Care for Vulnerable Children in California between 2001 and 2005
Bibliographic record
Abstract
OBJECTIVE: We examined population changes in access to care for children in California during a period of major efforts to improve access to care for children. METHODS: We used cross-sectional data on 36,010 children aged 0-19 years from the 2001 and 2005 California Health Interview Survey to assess population changes in access to care. We assessed changes in access by individual risk factors and a composite risk profile. RESULTS: In 2005, a smaller proportion of children were uninsured (8.2% vs. 10.9% in 2001), living in poverty (20.7% vs. 23.2% in 2001), and in families without a high school education (20.8% vs. 23.6% in 2001), all p<0.001. Before and after adjusting for these changes in risk, children were more likely in 2005 to have had a physician visit (odds ratio [OR] = 1.09, 95% confidence interval [CI] 1.07, 1.12) and dental visit (OR=1.11, 95% CI 1.08, 1.14). Children were slightly less likely in 2005 to have a regular source of care (OR=0.94, CI 0.91, 0.96). Children who had the highest risk profiles (> or = 4 risk factors) experienced the largest gains in access. For example, children with three and > or = 4 risk factors had gains in dental visits of 11 and 20 percentage points, respectively (p<0.001 for each), compared with < or = 3 percentage points for children with fewer risk factors. CONCLUSIONS: This study found improvements in physician and dental visits between 2001 and 2005 that were not fully explained by changes in insurance coverage or other demographic risk factors. Vulnerable children fared well during this period, suggesting that California may be making important and potentially replicable strides in reducing disparities.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".