Using touchscreen tablets to support social connections and reduce responsive behaviours among people with dementia in care settings: A scoping review
Bibliographic record
Abstract
The use of touchscreen tablets, such as the iPad, offers potential to support the person with dementia staying in a care setting, ranging from a long-term care home to an adult day programme. Although electronic devices are used among people with dementia, a comprehensive review of studies focusing on their impact and how they may be used effectively in care settings is lacking. We conducted a scoping review to summarize existing knowledge about the impact of touchscreen tablets in supporting social connections and reducing responsive behaviours of people with dementia in care settings. Our research team consists of patient partners and family partners, physicians, nurses, a medical student and an academic professor. A total of 17 articles were included in the review. Our analysis identified three ways in which touchscreen tablets support dementia care: (1) increased the person's engagement, (2) decreased responsive behaviours and (3) positive effect on enjoyment/quality of life for people with dementia. Lessons learned and barriers to the use of touchscreen tablets in the care of people with dementia are described. Overall, only a few studies delineated strategies that helped to overcome barriers to technology adoption in care settings. Knowledge translation studies are needed to identify effective processes and practical tips to overcome barriers and realize the potential of assistive technology in dementia care.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".