Fostering Autonomy: Exploring Innovative Dementia Care Environments in Four Countries
Bibliographic record
Abstract
Abstract Dementia is one of the major age-related diseases world-wide and challenges not only people living with dementia and their caregivers, but also societies and health care systems as a whole. To better meet the needs of people living with dementia, innovative care environments are being developed worldwide, as part of the wider community. Aiming to strengthen independence and slow down the cognitive decline, efforts concentrate on continuously engaging people living with dementia in activities of daily life, despite a progression of the disease. This international symposium will provide four presentations on innovative dementia care environments in four different countries, which stimulate and support autonomy of older people living with dementia in an active daily life. It examines diverse environmental elements of the care environment, including organizational, social and physical aspects, and what their impact is on residents and their caregivers. The first presentation explores Green Care Farms in the Netherlands, focusing on the impact of organizational environment, in particular culture and staff’s task integration, on residents’ autonomy. The second presenter discusses results from two Dementia Village models in Canada, exploring the environmental effect on residents’ quality of life and staff’s care practices. The third presentation examines the impact of the neighborhood-built environment on social health of older residents living with dementia in Germany, using Geographical Information Systen (GIS) analyses. Finally, the last presenter describes the effects of adaptation in the physical environment, i.e. smart, ambient bright light on residents with dementia living in nursing homes in the United States.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".