Technology-assisted home support of community-dwelling older adults living with dementia and their family caregivers: A ten-year systematic review
Bibliographic record
Abstract
Background: Dementia represents a primary global public health concern.Gerontechnology can support community-dwelling older adults living with dementia and their family caregivers to age in place. Research Aim:The current systematic review of the literature aims to provide a comprehensive description of technologies designed and tested to assist community-dwelling older adults living with dementia to do so.Methods: A systematic literature search was conducted in five different databases (CINAHL, Medline, PsycINFO, AgeLine, and Web of Science) and validated by two independent librarians.Using COVIDENCE software, two independent reviewers screened records from 2012 to 2022, available in English, French, and Spanish.Results: There were 1563 references published.After removing the duplicates, 877 titles and abstracts were screened and the full text of 132 studies was assessed for eligibility, with only 20 records meeting inclusion criteria.Conclusion: The technologies identified have overlapping functionalities including: a) behavioral monitoring, b) health monitoring, c) caregiver education, d) communication, e) tracking devices, and e) reminders, emergency warnings, and solutions for social isolation and daily activities support.We present a theoretical model to conceptualize gerontechnology use in people living with dementia and their family caregivers and provide recommendations for clinical practice, research, technological development, and public policy to foster the development and implementation of gerontechnology in the dementia continuum.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".