The Use of interRAI Scales Across Healthcare Settings: Building a Bridge Between Evidence and Practice
Bibliographic record
Abstract
Equitable and effective service and policy decisions require reliable evidence-based information; interRAI assessments offer objective data across broad health determinants for multiple purposes. This article aims to highlight selected embedded scales and algorithms and illustrate their prevalence across settings using Canadian data. Ten measures are described along with examples of subsequent use in predicting outcomes, adverse events, and resource utilization across diverse populations and jurisdictions. Prevalence rates for nine scales and algorithms were available across home and community care, Long Term Care (LTC), and inpatient settings. Higher rates of disability in function and cognition were seen in LTC and CCC, whereas palliative care has highest prevalence of health instability. Overlaps in key areas suggest the need to provide targeted services irrespective of setting. Overall, this article highlights the potential of scales and algorithms to capture key clinical information across the broader health determinants while minimizing assessment burden.
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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 | 0.269 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".