Assistive technology for people with dementia: an overview and bibliometric study
Bibliographic record
Abstract
BACKGROUND: This study presents an overview of recent research activities in assistive technology (AT) for people with dementia. Bibliometric studies are used to explore breadth and depth of different research areas, yet this method has not yet been fully utilised in AT research for people with dementia. METHODS: The bibliometric method was used for collecting studies related to AT. Based on inclusion/exclusion criteria, the AT studies with a focus on people with dementia are considered. STUDY SCOPE: The study is based on factors such as number of publications, citations per paper, collaborative research output, P-Index, major research and application areas and national dementia strategies. DATA COLLECTION: Data were collected from 2000 to 2014 in AT research. The top 10 countries are selected based on their research outputs. RESULTS: USA emerged as the leading contributor with 503 publications and an annual growth rate of 16%, followed by UK with 399 publications and growth rate of 22%. Germany with 101 publications is on the 6th place, but it has a higher citation rate 16.43% as compared to USA (13.34%). Although all 10 countries show good collaborative research output, Italy, Spain and the Netherlands emerge as top collaborative research contributors with high percentages (84%, 84% and 79%). All the top 10 countries, except Canada, Germany and Spain, have national dementia strategies in place. CONCLUSION: The overall analysis shows that USA and UK are working extensively in AT research for people with dementia. Both these countries also have well established national dementia strategies.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.147 | 0.205 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".