Facilitators and barriers to using AI-enabled robots with older adults in long-term care from staff perspective: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Assistive and service robots have been increasingly designed and deployed in long-term care (LTC) but little evidence guides their use. This scoping review synthesises existing studies on facilitators and barriers to using artificial intelligence (AI)-enabled robots with older adults in LTC settings. METHODS AND ANALYSIS: We will follow the Joanna Briggs Institute's scoping review methodology for the study, to be conducted from November 2023 to April 2024. We will focus on literature exploring the use of AI-enabled robots with older adults in an LTC setting from healthcare providers' perspectives. Three steps will be taken: (a) keywords and index terms will be identified from MEDLINE and CINAHL databases; (b) comprehensive searches will be conducted in MEDLINE, CINAHL, Embase, Web of Science, Scopus, AgeLine, PsycINFO, ProQuest and Google, using keywords and index terms identified in step (a); and (c) examining reference lists of the included studies and selecting items in the reference lists which meet the inclusion criteria. Searches for grey literature will also be conducted via Google. The results will be presented in a charting table and a narrative summary will be presented in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. ETHICS AND DISSEMINATION: Ethics approval and participation consent are not required because the data are publicly available. The results will be presented via a journal article and conference presentations.
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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 | 0.143 | 0.102 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.014 |
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".