The Affordable Care Act’s Coverage Expansions Will Reduce Differences In Uninsurance Rates By Race And Ethnicity
Bibliographic record
Abstract
There are large differences in US health insurance coverage by racial and ethnic groups, yet there have been no estimates to date on how implementation of the Affordable Care Act will affect the distribution of coverage by race and ethnicity. We used a microsimulation model to show that racial and ethnic differentials in coverage could be greatly reduced, potentially cutting the eight-percentage-point black-white differential in uninsurance rates by more than half and the nineteen-percentage-point Hispanic-white differential by just under one-quarter. However, blacks and Hispanics are still projected to remain more likely to be uninsured than whites. Achieving low uninsurance under the Affordable Care Act will depend on effective state policies to attain high enrollment in Medicaid and the Children's Health Insurance Program and the new insurance exchanges. Coverage gains among Hispanics will probably depend on adoption of strategies that address language and related barriers to enrollment and retention in California and Texas, where almost half of Hispanics live. If uninsurance is reduced to the extent projected in this analysis, sizable reductions in long-standing racial and ethnic differentials in access to health care and health status are likely to follow.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".