Understanding and guiding technology use in dementia: a pan-European mapping and consensus study
Bibliographic record
Abstract
Introduction Dementia is a leading cause of disability worldwide, and its prevalence is expected to rise significantly by the year 2050. Assistive technologies (AT) have emerged as promising tools to promote independence and quality of life. The COVID-19 pandemic prompted an increased uptake of AT among people with dementia, exposing important limitations in digital literacy, accessibility, and support. Methods This pan-European study mapped recent research initiatives involving digital technology use by people with dementia during the pandemic and synthesised a set of recommendations for supporting the use of AT by people with dementia, and its development, using the Delphi method. Results The mapping exercise identified 28 relevant projects, highlighting the types of technologies used during the pandemic and the settings in which they were implemented. Video-conferencing platforms were the most reported projects. More than half of the projects and initiatives ( n = 17) were adapted to include digital technologies due to the pandemic. The subsequent Delphi consensus study incorporated input from experts by experience and produced 18 evidence-based recommendations, adapted from this mapping exercise and a previous scoping review. Discussion Key findings emphasise involving people with dementia in technology design, ensuring equitable access, and providing adequate training and support. The recommendations offer a practical, consensus-based framework to improve the efficacy of AT adoption, with implications extending beyond pandemic contexts to improve dementia care globally.
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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 | 0.173 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.003 |
| 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".