Investigating the use of an autonomous robot assistant to improve the wellbeing of institutionalized older adults in Armenia
Bibliographic record
Abstract
Abstract Background Researchers investigated the use of an emotional support robot to improve the well‐being of institutionalized older adults in Armenia. The autonomous assistant called Robin the Robot assessed the cognitive and psychosocial needs of elders in a nursing home in Armenia. Robin the Robot is a semi‐autonomous robot that interprets facial expressions and conversational contextual clues to understand emotions from individuals, and then uses artificial intelligence to guide its responses and develop a therapeutic interaction with patients Method The twelve‐week study was a randomized control trial of using Robin the Robot as an intervention to improve well‐being of older individuals in long‐term nursing care facilities in Armenia. Upon enrollment, participants completed a brief demographics interview. Evaluation of study participants, both pre and post‐test included: the International Short‐Form of the Positive and Negative Affect Schedule (I‐PANAS‐SF)2; Montreal Cognitive Assessment (MoCA)8 and the Geriatric Anxiety Scale (GAS‐10)9 and Geriatric Depression Scale (GDS)10,11 Result The results of the study revealed Robin the Robot’s interventions improved the elder’s cognitive functioning. On the Montreal Cognitive Assessment test (MoCA), the elder’s score improved an average of 3.29 points per person. Their word recall had the largest numerical improvement. On the Geriatric Depression Test, Geriatric Anxiety Test and the PANAS Test (positive and negative affect test) there were no significant differences between the pre and post intervention, although a slight improvement on the Geriatric Depression Test, however no statistical difference. Conclusion The use of emotional support robots improved memory and cognitive functions of the elders, as well as slight improvement in mood. This project made a significant contribution in improving health outcomes for the elderly, bringing more attention to nursing facilities, and contributing to research on health and prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".