Relationship between State Medicaid Policies, Nursing Home Racial Composition, and the Risk of Hospitalization for Black and White Residents
Bibliographic record
Abstract
OBJECTIVE: To examine racial differences in the risk of hospitalization for nursing home (NH) residents. DATA SOURCES: National NH Minimum Data Set, Medicare claims, and Online Survey Certification and Reporting data from 2000 were merged with independently collected Medicaid policy data. STUDY DESIGN: One hundred and fifty day follow-up of 516,082 long-stay residents. PRINCIPLE FINDINGS: 18.5 percent of white and 24.1 percent of black residents were hospitalized. Residents in NHs with high concentrations of blacks had 20 percent higher odds (95 percent confidence interval [CI]=1.15-1.25) of hospitalization than residents in NHs with no blacks. Ten-dollar increments in Medicaid rates reduced the odds of hospitalization by 4 percent (95 percent CI=0.93-1.00) for white residents and 22 percent (95 percent CI=0.69-0.87) for black residents. CONCLUSIONS: Our findings illustrate the effect of contextual forces on racial disparities in NH care.
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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 | 0.004 |
| 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) | 0.002 | 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".