Development and Testing of the interRAI Acute Care: A Standardized Assessment Administered by Nurses for Patients Admitted to Acute Care
Bibliographic record
Abstract
Background:Underpinning standards for developing comprehensive care in hospital is the need to identify, early in the admission process, functional and psychosocial issues which affect patient outcomes. Despite the value of comprehensive assessment of patients on admission, the process is often sub-optimal due to a lack of standardized assessment practices. This project aimed to develop a concise, integrated assessment for patients admitted to acute care and test its psychometric properties.Methods:Two international expert panels of clinicians and health scientists collaborated to establish design parameters. Using clinical observations and a variety of derivative applications sourced from the interRAI research collaborative repository, the panels constructed a draft instrument to examine feasibility, resource requirements, and inter-rater reliability. Field testing was conducted in Australia and Canada. Next, the system was revised to its final form, the interRAI Acute Care, after feedback and review from international interRAI members.Results:Constructed using 56 items, the interRAI Acute Care required a median of 15 minutes to complete. Inter-rater reliability tested on 130 paired assessments was substantial to almost perfect for 78% of the clinical items and moderate for the remaining 22% of items. A subset of 30 items from the admission assessment comprised the discharge assessment.Discussion:The interRAI Acute Care has been shown to be an efficient nursing assessment instrument with good psychometric properties. Implementation in a digital environment will enable documentation and care planning to comply with standards for quality of care in the general adult hospital population.
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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 | 0.042 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".