Leveraging everyday technology for people living with dementia: a case study
Bibliographic record
Abstract
Purpose – The purpose of this paper is to present the self-described “journey” of a person with dementia (Brian; author 3) in his re-learning of old technologies and learning of new ones and the impact this had on his life. Design/methodology/approach – This is a single case study detailing the participant's experiences collaborating with a researcher to co-create methods of facilitating this learning process, which he documented in the form of an online blog and diary entries. These were analysed using NVivo to reveal the key themes. Findings – Brian was able to relearn previously used technologies and learn two new ones. This lead to an overarching theme of positive outlook on life supported by person-centredness, identity and technology, which challenged negative perceptions about dementia. Research limitations/implications – The paper provides an example of how learning and technology improved the life of one person with dementia. By sharing the approach the authors hope to encourage others to embrace the challenge of designing and developing innovative solutions for people with a dementia diagnosis by leveraging both current mainstream technology and creating novel bespoke interventions for dementia. Originality/value – The personal perspective of a person with dementia and his experiences of (re-) learning provide a unique insight into the impact of technology on his life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".