Supporting everyday cognition: Home Technology Tours with people living with dementia
Bibliographic record
Abstract
Abstract Background Current off‐the‐shelf technologies contain functionality which can support everyday cognition, such as storing telephone numbers and calendar reminders. These functions can benefit everyone, including people living with dementia. However, knowledge is limited about people living with dementia acquiring and using existing technologies and whether or how they are utilizing these functions. Method Technology Tours were completed in the homes of 9 individuals living with dementia, followed by semi‐structured interviews. As they went around their homes the individuals were asked to describe what technologies they used in each room, what they used them for, and how they acquired them. The tours and interviews were transcribed and analyzed using content analysis. Result Participants living with dementia described multiple technologies that they use in their everyday lives. These include televisions, stoves and mobility aids, alongside tablets, smart phones, and voice assistants (e.g. Alexa). Their reasons for using their devices were (i) maintaining meaningful activities, (ii) staying connected, and (iii) promoting independence, all of which influenced their well‐being. The usability of devices and safety features were also highlighted. Conclusion The findings indicate that people living with dementia use a wide range of technologies to support their everyday cognition. They adopt different functions for different aspects of their lives and would like to access more functionality. This is hindered by the complexity of learning to use and incorporate some potentially helpful devices into their daily lives. These findings can help to dispel myths about the interest and ability of people living with dementia to use digital tools, to encourage wider use of readily available functions and devices, and increased development of new cognitive supports.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".