InterRAI Acute Care Instrument for Seniors in Canadian Hospitals: Findings of an Inter-Rater Reliability Pilot Study
Bibliographic record
Abstract
BACKGROUND: Older adults are the biggest users of emergency departments and hospitals. However, healthcare professionals are often ill equipped to conduct comprehensive geriatric assessments causing missed opportunities for preventing adverse outcomes. PURPOSE: To evaluate the inter-rater reliability of the interRAI Acute Care (AC) instrument for hospitalized older adults in two acute care hospitals in Ontario, Canada. METHODS: This descriptive study focused on evaluating the interRAI AC instrument, which was designed to facilitate a comprehensive nursing assessment for hospitalized seniors. Sample characteristics were described, and Cohen's Kappa was calculated to derive the inter-rater reliability. Assessment times to complete the instrument were collected as well. RESULTS: The Cohen's Kappa score for the instrument was 0.96. Many older adults who were interviewed had several challenges, including multimorbidity, polypharmacy, and lack of home support. The average time required for nurses to complete the interRAI AC instrument was 22 min. CONCLUSIONS: The interRAI AC instrument is reliable for use by trained nurses to conduct a comprehensive assessment. This instrument offers a standardized and efficient approach to assess for care and intervention priorities and could prevent adverse outcomes in hospitalized older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".