Assistive Device Use among Community-Dwelling Older Adults: A Profile of Canadians Using Hearing, Vision, and Mobility Devices in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
There is increasing recognition that using assistive devices can support healthy aging. Minimizing discomfort and loss of function and increasing independence can have a substantial impact physically, psychologically, and financially on persons with functional impairments and resulting activity limitations, as well as on caregivers and communities. However, it remains unclear who uses assistive devices and how device use can influence social participation. The current analysis used CLSA baseline data from 51,338 older adults between the ages of 45 and 85. Measures of socio-demographic, health, and social characteristics were analyzed by sex and age groups. Weighted cross-tabulations were used to report correlations between independent variables and assistive device use for hearing, vision, and mobility. We found that assistive device use was higher among those who were of older age, had less education, were widowed, had lower income, and had poorer health. Assistive devices were used differently according to sex and social participation, providing insight into assistive device use for the well-being of older adults and their families.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".