Synthesis of interventions using an interRAI tool to guide care management and assess intervention efficacy in older adults: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: interRAI is a global collaboration of clinicians, researchers and policy-makers who have developed a suite of assessment tools to assess the health status and care needs of older adults in various settings (ie, home, long-term care, etc). We aim to determine how interRAI tools have been used as an intervention and to evaluate intervention efficacy in older adults (65+) across diverse healthcare settings. Importantly, given the deployment of interRAI primarily in high-income countries, we anticipate that the findings may have minimal relevance to low- and middle-income nations, where there is an immediate and urgent need for equity in geriatric assessment. METHODS AND ANALYSIS: To be included, all studies must satisfy our inclusion criteria, outlined by the population (ie, older adults and/or individuals providing some element of care to older adults), intervention (ie, randomised or non-randomised), comparator (ie, with or without one) and outcome (ie, how the interRAI formed the basis of a study intervention). Our search strategy is based on previous reviews of interRAI tools, our research and clinical experience, and the expertise of a specialised librarian. In addition to PubMed, we will conduct our search without date or language restrictions in Scopus, Embase,Cumulative Index to Nursing and Allied Health Literature (CINAHL), Academic Search Premier and PsycInfo. Study screening will employ a team-based approach, with Kappa statistics >0.8 indicating 'substantial' agreement and an acceptable threshold. Data extraction will capture the study ID and design, as well as sample characteristics and outcomes. Reporting will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews, with findings presented graphically and narratively. ETHICS AND DISSEMINATION: Ethics approval is not required. Our knowledge dissemination strategies include traditional research avenues (ie, manuscript publications). We will also create an infographic to disperse widely and leverage existing partnerships to provide community presentations. REGISTRATION DETAILS: https://doi.org/10.17605/OSF.IO/BGJKP.
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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 | 0.144 | 0.175 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.019 | 0.027 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.087 | 0.018 |
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".