Young adults seeking medical care: do race and ethnicity matter?
Bibliographic record
Abstract
KEY FINDINGS: Data from the National Health Interview Survey: 2008-2009. More than one-half of Hispanic young adults aged 20-29 years were uninsured in 2008-2009 compared with one-third of non-Hispanic black young adults and almost one-quarter of non-Hispanic white young adults. Among young adults aged 20-29 years, non-Hispanic white (66%) young adults were twice as likely as Hispanic (33%) young adults to have private health insurance coverage. Approximately equal percentages of Hispanic, non-Hispanic white, and non-Hispanic black young adults with private health insurance or Medicaid had a usual source of medical care. Uninsured non-Hispanic white (37%) and non-Hispanic black (33%) young adults were more likely to have unmet medical need than uninsured Hispanic (21%) young adults. Health care disparities among different racial and ethnic subgroups in the United States are of national concern. Health insurance is a key factor in the access to medical care services, and young adults in the United States aged 20-29 years are more likely than adults aged 30 years and over to lack health insurance coverage (1-4). A previous report has examined the differences in health insurance and access to health care by gender among young adults aged 20-29 years (5). This report focuses on the differences in health insurance and access to health care among Hispanic, non-Hispanic white, and non-Hispanic black young adults aged 20-29 years.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".