Disparities in Primary Care for Vulnerable Children: The Influence of Multiple Risk Factors
Bibliographic record
Abstract
OBJECTIVES: To analyze vulnerability as a profile of multiple risk factors for poor pediatric care based on race/ethnicity, poverty status, parent education, insurance, and language. Profiles are used to examine disparities in child/adolescent health status and primary care experience. DATA SOURCES: Cross-sectional data on 19,485 children/adolescents 0-19 years of age from the 2001 California Health Interview Survey. STUDY DESIGN: Multiple logistic regression models are used to examine risk profiles in relation to health status and three aspects of primary care: access (physician and dental visit; access surety), continuity (regular source of care), and comprehensiveness (i.e., health promotion counseling). PRINCIPAL FINDINGS: About 43 percent of (or 4.4 million) children in California have two or more risk factors (RF). Controlling for age and gender, more RFs is associated with poorer health status (i.e. percent reporting "excellent/very good" health: no RFs=81 percent, 1=71 percent, 2=57 percent, 3=45 percent, 4=35 percent, 5=28 percent, all p<.001). Controlling for health status, higher risk profiles is associated with poorer primary care access and continuity, but greater comprehensiveness of care. For example, higher risk profile children are less likely to have a regular source of care: one RF (prevalence ratio [PR]=0.92, confidence interval [CI]: 0.86-0.98), two (PR=0.77, CI: 0.69-0.84), three (PR=0.55, CI: 0.46-0.65), and four or more (PR=0.31, CI: 0.22-0.44), all p<.001. CONCLUSIONS: This study demonstrates a dose-response relationship of higher risk profiles with poorer child health status, access to, and continuity of primary care. Having gained access, however, adolescents with higher risk profiles are more likely to receive health promotion counseling. Higher profiles appear to be associated with greater barriers to accessing primary care for children in "fair or poor" health, suggesting that vulnerable children who have the greatest health care needs also have the greatest difficulty obtaining primary care.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".