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Record W4416228837 · doi:10.1177/08404704251391151

A Scoping Review of Interventions Using an interRAI Information System to Guide Care Management and Assess Intervention Efficacy in Older Adults

2025· article· en· W4416228837 on OpenAlexafffund
Nick W. Bray, Sydney MacNinch, N Nasiri, Jasmine Friedrich Yap, Ilona Barańska, Emmanuel Bagaragaza, George Heckman, Johanna De Almeida Mello, Katarzyna Szczerbińska, Caitlin McArthur

Bibliographic record

VenueHealthcare Management Forum · 2025
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsLawson Health Research InstituteDalhousie UniversityMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsPsychological interventionHealth informaticsInformation systemIntervention (counseling)Data extractionMEDLINEHealthcare systemMinimum Data Set

Abstract

fetched live from OpenAlex

interRAI instruments consist of clinical information systems able to support integrated care. Through a scoping review, we describe how interRAI instruments are used: (1) as interventions (implementation category) and (2) to evaluate interventions (efficacy category) in older adults. In accordance with the PRISMA-ScR framework, we searched 6 databases and conducted dual-independent screening, with conflicts resolved by a third reviewer. Data extraction followed an identical procedure. The review yielded 64 manuscripts, including 43 and 21 categorized as studies of efficacy or implementation, respectively. Findings indicate that interRAI systems are consistently utilized to evaluate or enhance participant-centred outcomes across diverse healthcare settings in 17 countries, with a particular emphasis on home and long-term care. interRAI is a versatile system with the potential to form the foundation of an integrated clinical information system. This review provides a basis for future research testing novel intervention strategies with interRAI systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.096
metaresearch head score (Gemma)0.264
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.096
Threshold uncertainty score0.509

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0960.264
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0070.010
Bibliometrics0.0330.033
Science and technology studies0.0020.002
Scholarly communication0.0060.006
Open science0.0040.005
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.067
GPT teacher head0.491
Teacher spread0.424 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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