Ambient Activity Technologies for Managing Responsive Behaviours in Dementia
Bibliographic record
Abstract
Many people living with dementia are under-stimulated and socially isolated. While there has been an increase in activities and programming based on recreational therapy and music therapy, such programs can cover only a fraction of the day for people with dementia and are resource demanding to execute. The result is that many people with dementia, who are institutionalized, are staying most of the day either in their rooms, sitting in communal areas, or wandering the hallways. A related problem is that people with dementia often have difficulty with social interactions and may become anxious or aggressive around people they do not recognize, or in situations they do not understand. Resulting responsive behaviours (e.g., hitting, screaming) may lead to overmedication and poor quality of life. Ambient Activity Technology (AAT) is a wall-mounted interactive tool designed for people with dementia. The AAT unit is available in the environment for easy access, and have been designed to augment existing programming and activities by providing self-accessed, engaging and personalized interactions at any time (24-hours per day, 7 days/week). AATs have been designed to reduce distress, in residents and caregivers, by substituting responsive behaviours and purposelessness with active and meaningful activities, distractions, and appropriate interventions. This paper describes the motivation behind the design and development of the AAT. The paper ends with a description of our summative evaluation research, which is currently in progress at several long-term care facilities in Ontario, Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".