Attitude and perceptions of older and younger adults towards ambient technology for assisted living.
Bibliographic record
Abstract
OBJECTIVE: Healthcare systems are challenged by the rapidly increasing number of older adults requiring services to maintain at-home independence. Technology, such as ambient sensing, has been identified as one potential solution to address these issues. This study's aim is twofold: (1) to explore the general perception of older and younger adults about the transformative role technology can play in their health care as they age, and (2) the generation of health solutions in home care. SUBJECTS AND METHODS: This study explores data collected from an online survey involving 367 participants from North America and South Asia. RESULTS: Our analyses yielded that the older adult participants had a generally positive attitude toward employing technologies and that younger adults were less concerned about the use of ambient sensing. Notably, however, they all reported relatively deep concerns about the potential use of homecare service technologies. Our results showed heterogeneity of technology literacy among older adults. CONCLUSIONS: Both older and younger adults perceive ambient technology for assisted living as a promising solution to enable older adults' at-home independence. Regardless of age, potential users of these technologies showed concerns with possible breaches of individual privacy, personal data, and personal health information.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".