Hospitalizations in Nursing Homes: Does Payer Source Matter? Evidence From New York State
Bibliographic record
Abstract
The objective of this study was to examine the reasons for different hospitalization rates between Medicaid and private-pay nursing home residents-to disentangle within-facility differences from across-facility variations in hospitalizations between these two types of residents. Multiple data sources (2003) for New York State were linked. Hospitalization was the dependent variable. Individual payer status was the main independent variable. Facilities were stratified into four groups by ownership status and bed-hold payment eligibility. We found both within-facility (Medicaid residents were more likely to be hospitalized than private-pay residents within a facility) and across-facility differences (facilities with a higher concentration of Medicaid residents were more likely to hospitalize their residents) controlling for individual and facility characteristics. The magnitude of within-facility differences varied with facility ownership and bed-hold eligibility. To reduce hospitalizations of Medicaid residents and to improve both quality of care and costs, policymakers may need to align Medicaid's and Medicare's incentives.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".