Optimizing the interRAI assessment tool in care planning processes for long-term care residents: a scoping review protocol
Bibliographic record
Abstract
Objective: In this scoping review, the objective is to chart and report on existing literature regarding how the interRAI assessment tool drives care planning processes for residents in long-term care settings. Introduction: Before COVID 19 pandemic, there were discussions here in Canada regarding how to improve the quality of care through the use of international resident assessment instruments (interRAI) in long-term care facilities. As many provinces and territories have deployed this digital tool, the provision of quality care in long-term care (LTC) for consistent health outcomes for the residents is still unattainable. In this scoping review, we report explicitly on how the interRAI tool can drive the coordination and implementation of care for consistent health outcomes. Inclusion criteria: This review will incorporate all studies on the use of interRAI in care planning processes for residents aged 65 years and over, in long-term care facilities. English language publications will be an inclusion factor. Excluded from the review are other interRAI assessment suites. Methods: The Joanna Briggs Institute methodology employed includes a three-step search strategy to i) identify keywords from the Cumulated Index of Nursing and Allied Health Literature, ii) conduct a comprehensive search using all of the initial and related keywords found across other selected databases, and iii) screen the titles and abstracts and the full texts review of the articles against the inclusion criteria by two independent reviewers. Extracted data will be presented in a tabular form with narrative that address the review objective. Keywords: interRAI; standardized data; long-term care; care planning
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 | 0.170 | 0.136 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.015 |
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".