Using the concept of activity space to understand the social health of older adults living with memory problems and dementia at home
Bibliographic record
Abstract
Globally as the population ages, the prevalence of dementia will increase. Simultaneously, there is a trend toward people ageing at home. Therefore, more people will be ageing at home with dementia, as opposed to institutional environments. In this context, there has been a recent shift in research exploring ways that people can live well with the consequences of the disease. As a part of this emerging research, the social and spatial aspects of the lives of people living with memory problems are becoming increasingly of interest. The aim of this article is to use the concept of activity space to examine the social health of older adults with memory problems and dementia who live at home. Activity space data were collected from seven older adults experiencing memory problems and living at home in the Netherlands. Using a mixed-methods approach, insight into their activity spaces were gained through walking interviews, 14 days of global positioning system (GPS) movement data, travel diary entries and in-depth interviews. The GPS data, travel diary data and interview transcripts were analyzed using a grounded visualization approach. Our findings show that participants interact independently in routine activity spaces but depend on others to participate in occasional activity spaces. Interactions within both these spaces contribute to the social health of older adults with memory problems and dementia who live at home. Additionally, participants used coping strategies and decision-making to maintain autonomy in daily life. The findings can inform dementia-friendly initiatives and social health care planning.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| 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".