Socially focused intelligent assistive technologies for caregiving for homebound older adults with cognitive impairment: a scoping review
Bibliographic record
Abstract
Background and Objectives: Being homebound with cognitive impairment (CI) presents major public health challenges, increasing home healthcare costs and contributing to caregiver burden, social isolation, and reduced quality of life. As loneliness and social isolation rise among this population, socially focused intelligent assistive technologies (SFIATs) have emerged as increasingly viable solutions. This scoping review examined the current body of literature on SFIATs and the barriers and facilitators to their support of homebound older adults with CI and their caregivers. Research Design and Methods: Using Arksey and O'Malley's framework, we searched 12 databases with MeSH terms related to older adults, CI, SFIATs, and homebound status. Data were analyzed descriptively using themes, with findings mapped across the socioecological framework. Results: Nineteen studies conducted in 12 high-income countries were included. Robots, tablets, telephones, computers, virtual avenues, and other smart devices were among the SFIATs utilized in caregiving for older individuals who were homebound or had CI. SFIATs facilitated social interaction, engagement, and connectedness among older adults and caregivers. Challenges and benefits associated with their use were evident at individual, interpersonal, community, organizational, and policy levels. Discussion and Implications: Research suggests significant potential in SFIATs, but their implementation faces multi-level challenges, often due to limited direct input from end-users, leading to concerns that impact their utilization. SFIAT development must adopt co-creation approaches to ensure its contextual appropriateness. Further research is needed, particularly in low- and middle-income countries, to understand the landscape, benefits, and challenges of SFIATs in diverse global settings.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".