The Effect of State Policies on Nursing Home Resident Outcomes
Bibliographic record
Abstract
OBJECTIVES: To test the effect of changes in Medicaid reimbursement on clinical outcomes of long-stay nursing home (NH) residents. DESIGN: Longitudinal, retrospective study of NHs, merging aggregated resident-level quality measures with facility characteristics and state policy survey data. SETTING: All free-standing NHs in urban counties with at least 20 long-stay residents per quarter (length of stay > 90 days) in the continental United States between 1999 and 2005. PARTICIPANTS: Long-stay NH residents INTERVENTIONS: Annual state Medicaid average per diem reimbursement and the presence of case-mix reimbursement in each year. MEASUREMENTS: Quarterly facility-aggregated, risk-adjusted quality-of-care measures surpassing a threshold for functional (activity of daily living) decline, physical restraint use, pressure ulcer incidence or worsening, and persistent pain. RESULTS: All outcomes showed an improvement trend over the study period, particularly physical restraint use. Facility fixed-effect regressions revealed that a $10 increase in Medicaid payment increased the likelihood of a NH meeting quality thresholds by 9% for functional decline, 5% for pain control, and 2% for pressure ulcers but not reduced use of physical restraints. Facilities in states that increased Medicaid payment most showed the greatest improvement in outcomes. The introduction of case-mix reimbursement was unrelated to quality improvement. CONCLUSION: Improvements in the clinical quality of NH care have been achieved, particularly where Medicaid payment has increased, generally from a lower baseline. Although this is a positive finding, challenges to implementing efficient reimbursement policies remain.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".