Supporting compassionate use of technology to support functioning in daily activities among people with cognitive decline – a scoping review
Bibliographic record
Abstract
Cognitive decline is becoming more prevalent as population ages. Technology offers solutions to help people with cognitive decline age in place. A compassionate approach to care can promote engagement in technology use by older adults with cognitive decline and equitable access. This scoping review summarized research literature on approaches to design and selection of technology that could support a compassionate approach to technology use for daily functioning among adults with cognitive decline and their care partners. We used the framework of Arksey and O’Malley. Key words capturing constructs of compassion, technology, and cognitive decline were searched in CINAHL, Medline, and PsycINFO. Peer-reviewed articles about the design for or use of technology by persons with cognitive decline or their care partners were included. Two reviewers screened and extracted data. Data informing compassionate technology use were analysed thematically. Fifty-five included articles represented a variety of technologies and purposes with ethics being the predominant perspective (n = 15). Analysis identified four categories: 1) Person- and care partner-centered approach, 2) Tailoring design to abilities, 3) Tailoring selection and application, and 4) Training and support. Using study findings, we developed a framework for compassionate use of technology for people living with cognitive decline 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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".