Research on the Emotional Impact of AI Care Robots on Elderly Living Alone
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.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".