Using Voice-Activated Technologies to Enhance Well-Being of Older Adults in Long-Term Care Homes
Bibliographic record
Abstract
Background and Objectives: Information communication technologies (ICTs) can enhance older adults' health and well-being. Most research on the use of voice-activated ICTs by older adults has focused on the experiences of individuals living in the community, excluding those who live in long-term care homes. Given evidence of the potential benefits of such technologies to mitigate social isolation and loneliness, more research is needed about their impacts in long-term care home settings. With this in mind, we evaluated impacts and engagement of older adults with voice- and touchscreen-activated ICTs in one long-term care home in Canada. Research Design and Methods: Interviews were conducted with older adults who were provided with a Google Nest Hub Max and with staff as part of a larger implementation study. Participants completed semistructured interviews before the technology was implemented, and again at 6 and 12 months. The interviews were recorded, transcribed, and analyzed using thematic analysis techniques. Results: We found that residents primarily used the technologies to engage in self-directed digital leisure and to engage with others both in and outside the home, and that this in turn enhanced their comfort, pleasure, and social connectedness. We also identified ongoing barriers to their engagement with the technology, including both personal and structural factors. Discussion and Implications: Our findings suggest that implementation of voice-activated ICTs can bring added value to broader efforts to improve well-being and quality of life in long-term care by enhancing choice, self-determination, and meaningful relationships.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".