Addressing Problems With Medicaid Home And Community-Based Services In The Age Of Rebalancing
Bibliographic record
Abstract
For nearly a half-century, Congress has created various legal authorities from among which states may select to provide Medicaid beneficiaries with long-term services and supports (LTSS) in home and community-based settings, instead of in institutions. This article describes these legal authorities and their genesis and contends that a simpler architecture would better serve program goals. In theory, state-level variation could shine a light on how Medicaid can best meet beneficiaries’ needs. Instead, a maze of federal legal authorities has resulted in inconsistent and inequitable access to care among beneficiaries and across geographies. The many programs are difficult to understand, even for researchers and regulators trying to measure outcomes. This opacity obscures the most effective paths forward as the demand for Medicaid LTSS grows. Simplifying Medicaid LTSS legal authorities could ease burdens for states and better serve beneficiaries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".