The effect of government payment methods on nursing home rehabilitation treatment and resident discharge outcomes.
Bibliographic record
Abstract
The research presented in this dissertation examines government payment systems for nursing home care in Canada and the United States. The focus for this research is on nursing home provider behavior and nursing home resident treatment. Three empirical studies were conducted to find out: (1) how responsive nursing home providers are to economic incentives associated with different government payment systems; (2) how different payment systems affect resident access to physical and occupational rehabilitation therapy; and (3) which payment systems lead to better or worse resident outcomes, measured by discharges from nursing home to home, hospital, or death. Resident level data were obtained from the Minimum Data Set - Resident Assessment Instrument for nursing homes (MDS). Data representing all nursing home residents in 8 states, and the Canadian province of Ontario are used to examine these questions. The central findings of the three studies are that: (1) nursing homes differentiate between nursing home residents on the basis of payment source; (2) nursing homes are highly responsive to economic incentives associated with different payment systems; and (3) even where some payment methods improve resident access to rehabilitation therapy, it is not obvious that the same payment methods also lead to improved resident outcomes. Though there are substantive limitations to this research, the results are consistent across a variety of different study populations and model specifications. This study provides the only empirical evidence of the effect of payment incentives on nursing home rehabilitation 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 | 0.012 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".