Smartphone for smart living: Using new technologies to cope with everyday limitations in semantic dementia
Bibliographic record
Abstract
New technologies have considerable potential to support people with semantic dementia-a form of progressive aphasia-in their everyday lives, but evidence is still sparse. The first objective of the study was to document day-to-day compensation strategies, including the use of a smartphone, in ND, a 56-year-old man with semantic dementia. The second objective was to explore if, 5 years after receiving his diagnosis, ND could still learn new smartphone functions. Results for objective 1 showed that ND had adopted a large number of compensation mechanisms in his everyday life, and expanded the use of one application he had learned 4 years earlier. Results for objective 2 showed that, with an errorless learning approach, he learnt to effectively use 10 smartphone functions. He was also able to verbalise semantic knowledge about those functions and still used 40% of them in daily life 6 months post-intervention. He particularly appreciated note-taking, and spontaneously expanded his abilities in using this function's features in order to reduce his semantic difficulties. This study shows the potential of new mobile technologies for semantic dementia, how they can be adapted and modified as the disease progresses, and how some patients can creatively use external technological aids.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".