The Importance of Trust in the Adoption and Use of Intelligent Assistive Technology by Older Adults to Support Aging in Place: Scoping Review Protocol
Bibliographic record
Abstract
BACKGROUND: Intelligent assistive technologies that complement and extend human abilities have proliferated in recent years. Service robots, home automation equipment, and other digital assistant devices possessing artificial intelligence are forms of assistive technologies that have become popular in society. Older adults (>55 years of age) have been identified by industry, government, and researchers as a demographic who can benefit significantly from the use of intelligent assistive technology to support various activities of daily living. OBJECTIVE: The purpose of this scoping review is to summarize the literature on the importance of the concept of "trust" in the adoption of intelligent assistive technologies to assist aging in place by older adults. METHODS: Using a scoping review methodology, our search strategy will examine the following databases: ACM Digital Library, Allied and Complementary Medicine Database (AMED), Cumulative Index to Nursing and Allied Health Literature (CINAHL), Medline, PsycINFO, Scopus, and Web of Science. Two reviewers will independently screen the initial titles obtained from the search, and these results will be further inspected by other members of the research team for inclusion in the review. RESULTS: This review will provide insights into how the concept of trust is actualized in the adoption of intelligent assistive technology by older adults. Preliminary sensitization to the literature suggests that the concept of trust is fluid, unstable, and intimately tied to the type of intelligent assistive technology being examined. Furthermore, a wide range of theoretical lenses that include elements of trust have been used to examine this concept. CONCLUSIONS: This review will describe the concept of trust in the adoption of intelligent assistive technology by older adults, and will provide insights for practitioners, policy makers, and technology vendors for future practice.
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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 | 0.089 | 0.114 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".