Caregivers’ experiences with the selection and use of assistive technology
Bibliographic record
Abstract
Qualitative data from a mixed-methods clinical trial are used to examine caregivers' experiences with the selection and use of assistive technology to facilitate care recipients' independence. Through a thematic analysis of interviews from 27 caregivers, three broad themes were identified. "A partial peace of mind" described the generally positive psychological impacts from assistive technology, mainly reduced stress and a shift in caregiving labour from physical tasks to a monitoring role. "Working together" explored the caregivers' experiences of receiving assistive technology and the sense of collaboration felt by caregivers during the intervention process. Finally, "Overcoming barriers" addressed two impediments to accessing assistive technology: lack of funding and appointment wait times for service providers. The findings suggest that assistive technology provision by prescribers plays a beneficial role in the lives of caregivers, but access to such benefits can be hampered by contextual constraints. Implications for rehabilitation The study findings have a number of implications for rehabilitation practice: Family caregivers can be instrumental in determining what assistive technology is needed and then procured. Their involvement in the selection process is desirable because assistive technology may have both positive and negative impacts on them, and they themselves may use the devices chosen. Involving family caregivers as more active partners in the process of assistive technology provision may represent a greater time investment in the short term, but may contribute to better long-term outcomes for care recipients and caregivers as well. Limited access to funding and long appointment wait times are potential barriers to obtaining necessary assistive technologies.
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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 | 0.064 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".