Affirming the Value of the Resident Assessment Instrument: Minimum Data Set Version 2.0 for Nursing Home Decision-Making and Quality Improvement
Bibliographic record
Abstract
BACKGROUND: We examined the agreement over time of the physical functioning domains of the Resident Assessment Instrument: Minimum Data Set Version 2.0 (RAI-MDS) and the Functional Independence Measure (FIM) in nursing home residents with dementia. METHODS: We completed a secondary analysis of data from a longitudinal quasi-experimental study of residents who could transfer independently or with the assistance of one person. FIM assessments were completed at up to three time points by researchers using interviews. RAI-MDS assessments, completed by nursing home staff, were matched to the FIM assessment by nearest time. FIM and RAI-MDS assessments were correlated based on time between assessments using Pearson's correlation. Items for activities of daily living (ADL) from the RAI-MDS were rescaled using two previously published crosswalks. Motor and ADL subscales were also used, containing eight and six items, respectively. RESULTS: A total of 362 paired interviews and assessments were collected from 130 residents. The mean scores and standard deviations were as follows: FIM: 19.64 (7.60); William's RAI-MDS crosswalk: 18.04 (5.25); and Velozo's RAI-MDS crosswalk: 18.09 (6.50). Using both crosswalks, most items showed medium (r > 0.3) or large (r > 0.5) correlations, even at greater than 41 days between assessments. Subscales showed large correlations for all time intervals for both crosswalks. CONCLUSIONS: The RAI-MDS remains stable when data are collected greater than 41 days from the FIM assessment. These findings should add confidence in the RAI-MDS data and its clinical utility.
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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 | 0.110 | 0.197 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".