Using the RUG—III classification system for understanding the resource intensity of persons with intellectual disability residing in nursing homes
Bibliographic record
Abstract
Since 1991, the Minimum Data Set 2.0 (MDS 2.0) has been the mandated assessment in US nursing homes. The Resource Utilization Groups III (RUG-III) case-mix system provides person-specific means of allocating resources based on the variable costs of caring for persons with different needs. Retrospective analyses of data collected on a sample of 9707 nursing home residents (2.4% had an intellectual disability) were used to examine the fit of the RUG-III case-mix system for determining the cost of supporting persons with intellectual disability (intellectual disability). The RUG-III system explained 33.3% of the variance in age-weighted nursing time among persons with intellectual disability compared to 29.6% among other residents, making it a good fit among persons with intellectual disability in nursing homes. The RUG-III may also serve as the basis for the development of a classification system that describes the resource intensity of persons with intellectual disability in other settings that provide similar types of support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".