A Scoping Review of Technology Acceptance Models and Theories for Sustainable Use in People With Dementia
Bibliographic record
Abstract
Digital health technologies offer promising solutions for enhancing the quality of life for people with dementia, but they have some drawbacks. The aim of this scoping review was to explain the factors influencing acceptance of such technologies, by identifying and exploring the empirical support for different theoretical models. Following the PRISMA-ScR checklist, data were collected from PubMed, Web of Science, Scopus, PsycInfo, and IEEE Xplore. The review explored technology acceptance in people with dementia through any model or theory including the factors that could potentially determine acceptance. Thirty-one articles were included in the review. Different perspectives, approaches, and modifications of well-known technology acceptance models and theories regarding their underlying constructs were reported in the articles selected. Perceived usefulness, perceived ease of use, and social influence, the most studied constructs, have been found to have the greatest impact on the acceptance of different technologies in people with dementia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".