Informing the development of assistive technologies for persons with dementia by connecting financial measures of wealth to perceptions of task dependence
Bibliographic record
Abstract
BACKGROUND: Older adults with dementia have been targeted toward the development of assistive technologies intended to facilitate aging in place. Researchers have documented financial and occupation strain for the caregiver and the financial limitations experienced by persons with dementia. These factors constitute a potential hindrance to the use and applicability of assistive technologies; technologies that may reduce caregiver burden, allow more time for paid work, and, in consequence, reduce occupational strain. OBJECTIVE: To unpack how financial burden, operationalized as direct (e.g., income) and indirect (e.g., caregiver education, employment status) measures of wealth and assets, affect the perceived independence of people with dementia. METHODS: We draw on data collected through a cross-Canada survey of caregivers to develop a set of predictive models of care-recipient task independence. RESULTS: Our findings suggest that said measures of wealth can predict task independence, and more complicated or instrumental daily tasks (e.g., shopping, driving) are perceived as being those with which care recipients need most assistance. CONCLUSIONS: Considering the economical and emotional obstacles that affect both the caregiver and the care recipient, the development of assistive technologies that would be both financially realistic and assistive for this population in these instrumental daily tasks is warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".