Health-Assistive Smart Homes for Aging in Place: Leading the Way for Integration of the Asian Immigrant Minority Voice
Bibliographic record
Abstract
Caring for America's aging population is a complex humanitarian issue. The number of older adults is expected to increase to 98.5 million by 2060 with a 295% growth in foreign-born older adults, including Asian immigrants. Most older adults will have one or more chronic conditions and 95% of healthcare costs will be attributed to caring for these conditions. Among Asian Americans, common chronic conditions include respiratory disease, cancer, cardiovascular disease, and pain. The National Institutes of Health, Institute on Aging, and National Science Foundation call for innovative technologies to be developed by multidisciplinary teams to address these concerns. Asian community leaders at Asian Health & Service Center and community members in Oregon identified the use of health-assistive technologies as a priority for potentially reducing stress and improving quality of life for both older adults and their caregivers. The purpose of this article is to introduce nurses and healthcare workers, advocating for the interests of Asian/Pacific Island community members, to the innovative health-assistive smart home. The health-assistive smart home uses artificial intelligence to identify and predict health events. Inclusion of minority persons' data in the development of artificial intelligence has been generally overlooked. This may result in continued health inequities and is incompatible with the goals of global health. Integration of minority voices while exploring the efficacious use of the health-assistive smart home is of significant value to minority populations. Asian immigrant older adults engaging in smart home research and development will enhance the cultural and technical safety of future devices. Asian families may be particularly interested in smart homes for extending independence because they place an emphasis on collective culture and family-based care. Community engagement of stakeholders and steadfast leadership are needed so that future technologies used in healthcare delivery are both technically and culturally sound. A community-engaged research approach promotes community empowerment that is responsive to community identified priorities and is a good fit for studying adoption of smart home monitoring for health-assistance.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".