Interviews with family caregivers of older adults: Their experiences of care and the integration of assistive technology in care
Bibliographic record
Abstract
BACKGROUND: Consequences related to caregiving are multidimensional. Ways to reduce burden should be investigated, such as the use of assistive technology (AT). AT can decrease family caregiver burden, but there are multiple barriers to their uptake. A mixed-method project was launched to understand the needs of family caregivers and how could technology provide support. This study draws from qualitative data of this project. OBJECTIVE: To understand the experience of care provision and the integration of AT in the care provided by family caregivers. METHODS: Participants had to have provided care to an older adult or be an older adult providing care. Data collection consisted of semi-structured interviews on the caregiving situation and use of AT. A thematic content analysis was conducted. RESULTS: Fifty-nine family caregivers were recruited. Three main themes were identified: ‘Responsibilities of Caregiving’ described that family caregivers assisted in all areas of their care recipient’s life. ‘Caregivers’ Challenges and Rewards’ portrayed the challenges experienced by family caregivers and identified positive caregiving activities. ‘Strategies to Address Responsibilities and Challenges’ illustrated two main strategies to face challenges: sharing caring and using AT. CONCLUSION: The variability in care provision and challenges encountered should be taken into consideration when developing AT.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".