Indian Health Service: Most American Indians and Alaska Natives Potentially Eligible for Expanded Health Coverage, but Action Needed to Increase Enrollment
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "GAO estimates, on the basis of recent U.S. Census Bureau data, that most American Indians and Alaska Natives will be potentially eligible for either expanded or new coverage options created by the Patient Protection and Affordable Care Act (PPACA). These options include expanded eligibility for Medicaid--the federal-state program for certain low-income individuals--and eligibility for the Health Insurance Exchanges (Exchanges), which are marketplaces where health insurance plans can be compared and purchased. While it is still unclear which states will opt to expand Medicaid, their decisions may affect a large proportion of American Indians and Alaska Natives, as GAO estimates that potential new enrollment could include about a quarter of this population. For example, in the Oklahoma City area--one of the Indian Health Service's (IHS) 12 federally designated service areas--tens of thousands of American Indians and Alaska Natives could be affected by the state of Oklahoma's decision not to expand its Medicaid program. For the Exchanges, GAO found that more than one-third of American Indians and Alaska Natives are potentially eligible for premium tax credits in the Exchanges--which help offset the cost of premiums for low-income individuals--and nearly one-third are below the income threshold for cost-sharing exemptions, which are limited to enrolled members of federally recognized tribes."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.023 |
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".