Care Planning Across the Health System: Intersectoral Application of the interRAI Assessment System
Bibliographic record
Abstract
Older adults living with frailty and multimorbidity interact with multiple care providers and health settings, resulting in fragmented care and information discontinuity. Standardized assessments potentiate integrated care by communicating consistent measures of health information between sectors and providers. We use a pragmatic case example of a theoretical medically complex older adult to illustrate use of interRAI standardized assessments throughout the health journey. The case example represents the assessment findings of a patient accessing care through primary care, the emergency department, home/community care and long-term care. A suite of assessment instruments embedded with decision support algorithms guides nursing care decisions, while a common language and standardized assessment items support effective communication and collaboration among the health team. Successful adoption of integrated and comprehensive assessment tools requires training, engagement, and time to embed processes into practice. interRAI assessments enable integration through a common language, aligning successive assessments across the care continuum.
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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 | 0.043 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".