1HORIZONS Project A Profile of Hispanic Elders
Bibliographic record
Abstract
The Medicare program has largely achieved equal access to medical care services by elder and disabled Americans. The program’s significant contributions to eliminating disparities in access to medical care services for low income, black and Hispanic elders are often forgotten. Medicare is often the first and sometimes the only health insurance coverage that Latinos have ever had in their lives. Medicare provides health insurance coverage to about two mil-lion Latino elderly, about five percent of all elderly in America today. By 2025, the Census Bureau estimates that one in six elderly Americans will be Latino. Medicare’s almost universal coverage of elders has improved Latino access to medical treat-ment. Yet many Latinos have not taken full advantage of program benefits for a variety of reasons, including a lack of knowledge about the program, its benefits, and options for care delivery. In an effort to better inform Latino elders about Medicare, HCFA is engaged in a set of activi-ties to reach out to Latino beneficiaries to identify the issues they need to know about and to better supply them with information they require to use the program. This is the first in a series of reports using primarily the Current Population Survey of the continental United States aimed at understanding the target population for these communications efforts. Latinos reside throughout the United States but are highly concentrated in a few regions and major metropolitan areas. For example, almost a quarter of the entire Latino population lives in the Los Angeles metropolitan area. Other major metropolitan areas with high concentra-
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.009 |
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".