Physical Rehabilitation following Medicare Prospective Payment for Skilled Nursing Facilities
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of the Medicare prospective payment system (PPS) for skilled nursing facilities (SNF) on the delivery of rehabilitation therapy to residents. DATA SOURCES: Resident-level data are based on the Resident Assessment Instrument Minimum Data Set for nursing facilities. All elderly residents admitted to SNFs in Michigan and Ohio in 1998 and 1999 form the study population (n=99,952). STUDY DESIGN: A differences-in-differences identification strategy is used to compare rehabilitation therapy for SNF residents before and after a change in Medicare SNF payment. Logistic and linear regression analyses are used to examine the effect of PPS on receipt of physical, occupational, or speech therapy and total therapy time. DATA EXTRACTION: Data for the present study were extracted from the University of Michigan Assessment Archive Project (UMAAP). One assessment was obtained for each resident admitted to nursing facilities during the study period. PRINCIPAL FINDINGS: The introduction of PPS for all U.S. Medicare residents in July of 1998 was associated with specific targeting of rehabilitation treatment time to the most profitable levels of therapy. The PPS was also associated with increased likelihood of therapy but less rehabilitation therapy time for Medicare residents. CONCLUSIONS: The present results indicate that rehabilitation therapy is sensitive to the specific payment incentives associated with PPS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".