Research on the Emotional Impact of AI Care Robots on Elderly Living Alone
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2023, the population of people aged 60 and above in China accounts for 19.8% of the total population. As society progresses towards an increasingly aged demographic, there is a growing focus on the well-being of elderly individuals. Both physical and mental declines have become more prevalent among the elderly, leading to increased levels of depression and psychological vulnerability. The number of elderly individuals suffering from depression-related conditions is on the rise, and there is a growing issue of elderly ndividuals living alone. To address this challenge, artificial intelligence (AI) is being employed to assist in providing care to the elderly. Intelligent robots are used to aid in therapy for those in need among the elderly population. In order to prevent conditions like dementia in the elderly, AI-powered robots are used to provide personalized care and assess their health status. Different types of care and treatment are administered to various groups of elderly individuals based on their specific needs. The analysis of AI products that incorporate anthropomorphic elements plays a positive role in satisfying the emotional needs of the elderly and related design aspects. With the increase in human lifespan, the role of artificial intelligence in the silver industry is accelerating, and there are high expectations for broader developments in the field of intelligent robotics in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it