Unforeseen Consequences: Medicaid and the Funding of Nonprofit Service Organizations
Bibliographic record
Abstract
Medicaid reimbursements have become a key source of funding for nonprofit social service organizations operating outside the medical care sector, as well as an important tool for states seeking resources to fund social service programs within a devolving safety net. Drawing on unique survey data of more than one thousand nonprofit social service agencies in seven urban and rural communities, this article examines Medicaid funding of nonprofit social service organizations that target programs at working-age, nondisabled adults. We find that about one-quarter of nonprofit service organizations--mostly providers offering substance abuse and mental health treatment in conjunction with other services--report receiving Medicaid reimbursements, although very few are overly reliant on these funds. We also find Medicaid-funded social service nonprofits to be less accessible to residents of high-poverty neighborhoods or areas with concentrations of black or Hispanic residents than to residents of more affluent and white communities. We should expect that the role of Medicaid within the nonprofit social service sector will shift in the next few years, however, as states grapple with persistent budgetary pressures, rising Medicaid costs, and decisions to participate in the Medicaid expansion provisions contained within the 2010 Patient Protection and Affordable Care Act.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".