Reducing the Burdens of Paid Caregivers of Older Adults by Using Assistive Technology: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Paid caregivers are needed to support older adults, but caregiver burden contributes to high turnover rates. Assistive technologies help perform activities of daily living (ADLs) and can reduce caregiver burden, but little is known about how they impact paid caregivers. OBJECTIVE: This scoping review provides an overview of evidence on using assistive technology to reduce burdens on paid caregivers working with older adults. DESIGN: The review was conducted from May to August 2022. The eligibility criteria included: (1) publication within 5 years in peer-reviewed journals, (2) investigation of assistive technology, (3) main participants include paid caregivers supporting older adults, and (4) describing impacts on caregiver burden. Searches were conducted in 6 databases, generating 702 articles. The charted data included (1) country of study, (2) participant care roles, (3) study design, (4) main outcomes, and (5) types of assistive technology. Numerical description and qualitative content analysis of themes were used. RESULTS: Fifteen articles reporting on studies in 9 countries were retained for analysis. Studies used a variety of quantitative (8/15), qualitative (5/15), and mixed (2/15) methods. Technologies studied included grab bars and handrails, bidet seats, bed transfer devices, sensor and monitoring systems, social communication systems, and companion robots. Articles identified benefits for reducing stress and workload, while paid caregivers described both positive and negative impacts. CONCLUSIONS: Literature describing the impact of assistive technology on paid caregivers who work with older adults is limited and uses varied methodologies. Additional research is needed to enable rigorous evaluation of specific technologies and impacts on worker turnover.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".