Medicaid: Enrollment and Expenditures for Qualified Individual and Transitional Medical Assistance Programs
Bibliographic record
Abstract
Correspondence issued by the Government Accountability Office with an abstract that begins "The QI program enrolled about 426,000 individuals nationwide in 2009--the most recent year for which comprehensive enrollment data were available--with expenditures of about $431 million. While QI enrollment increased 30 percent from fiscal year 2006 to fiscal year 2009, program expenditures increased at a slightly faster rate, rising 39 percent during this time. On average, one quarter of individuals potentially eligible for the QI program were enrolled during fiscal years 2006 through 2009. However, the percentage of eligible individuals enrolled in the program climbed from 21 percent in fiscal year 2006 to 29 percent in fiscal year 2009. CMS does not have comprehensive national data on TMA enrollment and expenditures; the 41 states that had enrollment data reported that over 3.5 million individuals were enrolled in TMA in 2011, the most recent year for which complete enrollment data were reported by the highest number of states. Fewer states were able to report TMA expenditure data; however, 32 states reported TMA total expenditures of about $3.9 billion in 2011."
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".