Acceptance, adoption, and usability of information and communication technologies for people living with dementia and their care partners: a systematic review
Bibliographic record
Abstract
PURPOSE: This review aims to examine the instruments, approaches, scales, or assessment tools used to evaluate technology acceptance, technology adoption, and usability of information and communication technologies (ICTs) for people living with dementia and their care partners. METHODS: A systematic literature review was conducted. Studies that explored the use of instruments, approaches, scales, or assessment tools to evaluate the technology acceptance and usability of ICTs for people living with dementia and their care partners were identified through five databases: Medline, EMBASE, CINAHL, Web of Science, and Scopus. RESULTS: We included 74 out of 2182 papers. The most common scales used included the System Usability Scale (SUS) (11%), the ISONORM 9241/10 Questionnaire (4%), and the Post-Study System Usability Questionnaire (PSSUQ) (4%). Most (59%) of the included approaches, however, were bespoke (i.e., created by the authors for a particular study) and were not named. The approaches or tools used to assess technology acceptance, technology adoption, and usability of ICTs that applied to people living with dementia had an average of 15 items and used an average of 5.23 scale points. CONCLUSION: There is no clear, standardised approach for assessing the technology acceptance, technology adoption, and usability of ICTs for people living with dementia and their care partners. The findings of this review may be used by academics to design and implement improved and more consistent assessment tools to assess technology acceptance, technology adoption, and usability of ICTs for people living with dementia and their care partners.IMPLICATIONS FOR REHABILITATIONThe number of ICTs for people with dementia and their care partners that can be used for rehabilitation is increasingThe most commonly recognized assessment tools used in this study were the SUS, ISONORM 9241/10, and PSSUQ questionnaires.For the custom assessment tools, the average number of items included in this study was 15 with five-point bidirectional labelling.There is no clear, standardized approach for assessing the technology acceptance, technology adoption, or usability of ICTs for people with dementia and their care partners.
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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 | 0.021 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".