Effects of an Assistive Technology Intervention on Older Adults with Disabilities and Their Informal Caregivers
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to demonstrate experimentally that an assistive technology (AT) intervention improves older AT users' activity performance and satisfaction with activity performance and decreases their caregivers' sense of burden. DESIGN: This study was a delayed intervention, randomized control trial. Baseline data were collected on 44 community-dwelling AT user-caregiver dyads in Vancouver, British Columbia, and Montreal, Quebec. The primary outcome measures for AT users were the satisfaction and accomplishment scales from the Assessment of Life Habits. The primary outcome measure for caregivers was the Caregiver Assistive Technology Outcome Measure, which assessed burden associated with dyad-identified problematic activities. RESULTS: After the intervention, assistance users in the immediate intervention group reported significantly increased satisfaction with activity performance (P < 0.001) and improved accomplishment scores (P = 0.014). Informal caregivers in the immediate intervention group experienced significantly decreased burden with the dyad-identified problematic activity (P = 0.013). Participants in the delayed intervention group experienced similar benefits after the intervention. Improvements for both groups were mostly maintained 4 mos after the conclusion of the intervention. CONCLUSIONS: This is the first experimental study to demonstrate that the provision of AT decreases caregiver burden. If confirmed and extended by subsequent research, the findings have significant policy and practice implications and may enable health care providers to advocate for improved access to AT provision and the related follow-up services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".