Use and uptake of technology by people with dementia and their supporters during the COVID-19 pandemic
Bibliographic record
Abstract
OBJECTIVE: This rapid review aims to identify the types of technologies used by people with dementia and their supporters during the COVID-19 pandemic, and the issues which influenced technology adoption within their usual care routines. METHODS: PubMed, PsychInfo, Scopus, and Cochrane COVID reviews were searched to identify peer-review studies published since 2020. A total of 18 studies were included and synthesised thematically. RESULTS: = 12) focused on digital off-the-shelf and low-cost solutions, such as free video conferencing platforms, to access care, socialise or take part in interventions. Whilst often well-accepted and associated with positive outcomes (such as improved social connectedness), lack of digital literacy or support to use technologies, limited access to appropriate technology, individuals' physical, cognitive, or sensory difficulties, were highlighted and likely to threaten the adoption of these solutions. The quality of the evidence was mixed, neither very robust nor easily generalisable which may be attributed to the challenges of conducting research during the pandemic or the need to rapidly adapt to a new reality. CONCLUSION: While COVID-19 has fast-tracked the adoption of technology, its use is likely to continue beyond the pandemic. We need to ensure this technology can leverage dementia support and care and that people with dementia are enabled and empowered to use it.
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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 | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".