Bibliographic record
Abstract
The Medicare Current Beneficiary Survey (MCBS) is a powerful tool for analyzing the Medicare population. Using MCBS data from the 2000 Access to Care File, differences in the composition of the population enrolled in Medicare risk HMOs and of those in the same geographic areas who remained in fee-for-service (FFS) were examined. The results show that differences in the population reflect different rates of managed care enrollment among social, economic, and demographic groups of Medicare beneficiaries. In calendar year 2000, two-thirds of Medicare enrollees lived in a geographic area served by at least one risk health maintenance organization (HMO) (Figure 1). One-quarter of those people—or 17 percent of all enrollees—were enrolled in a risk HMO during the year, and the other three-quarters remained in the Medicare FFS Program. Figure 1 Percentage of Medicare Beneficiaries in Medicare Risk Health Maintenance Organization (HMO) and Fee-For-Service (FFS), 2000 The MCBS is a continuous survey of a nationally representative sample of Medicare enrollees. Survey respondents are interviewed three times each year for 3 years, plus an opening and closing interview. Information is collected from them about social, economic, and demographic life factors, as well as use of health care services and financing of those services. This survey information is combined with administrative data on use of and payment for medical care through the Medicare Program. For additional information about the MCBS, visit our Web site at http://www.cms.hhs.gov/mcbs.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".