Reliability of the interRAI suite of assessment instruments: a 12-country study of an integrated health information system
Bibliographic record
Abstract
BACKGROUND: A multi-domain suite of instruments has been developed by the interRAI research collaborative to support assessment and care planning in mental health, aged care and disability services. Each assessment instrument comprises items common to other instruments and specialized items exclusive to that instrument. This study examined the reliability of the items from five instruments supporting home care, long term care, mental health, palliative care and post-acute care. METHODS: Paired assessments on 783 individuals across 12 nations were completed within 72 hours of each other by trained assessors who were blinded to the others' assessment. Reliability was tested using weighted kappa coefficients. RESULTS: The overall kappa mean value for 161 items which are common to 2 or more instruments was 0.75. The kappa mean value for specialized items varied among instruments from 0.63 to 0.73. Over 60% of items scored greater than 0.70. CONCLUSION: The vast majority of items exceeded standard cut-offs for acceptable reliability, with only modest variation among instruments. The overall performance of these instruments showed that the interRAI suite has substantial reliability according to conventional cut-offs for interpreting the kappa statistic. The results indicate that interRAI items retain reliability when used across care settings, paving the way for cross domain application of the instruments as part of an integrated health information system.
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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 | 0.051 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| 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".