Using a Robot to Address the Well-Being, Social Isolation, and Loneliness of Care Home Residents via Video Calls: Qualitative Feasibility Study
Bibliographic record
Abstract
Background: About 40,000 people are living in Norwegian care homes, where a majority are living with a dementia diagnosis. Social isolation and loneliness are common issues affecting care home residents' quality of life. Due to visitation restrictions during the pandemic, residents and family members started using digital solutions to keep in contact. There is no framework or guidelines to inform the uptake and use of technologies in the care home context, and this often results in non-adoption and a lack of use after the introduction phase. Hence, there is a great need for research on the feasibility of a robot that can facilitate video communication between residents and family members. Objective: This study aimed to (1) introduce video communication through a robot to address social isolation and loneliness in a care home during a period of 6 weeks and (2) identify elements central to the feasibility concerning testing and evaluating the use of the robot. Methods: Three focus group interviews were undertaken: 1 with family members (n=4) and 2 with care staff (n=2 each). The informants were purposely selected to ensure that they had the proper amount of experience with the robot to have the ability to inform this study's objectives. The focus group interviews were tape-recorded and transcribed verbatim, then subsequently analyzed using systematic text condensation. Results: The data analysis of focus group interviews and individual interviews resulted in three categories: (1) organizing the facilitation of video calls, (2) using a robot in dementia care, and (3) user experience with the robot. Conclusions: Video communication in care homes is a feasible alternative to face-to-face interactions, but it depends on organizational factors such as information flow, resources, and scheduling. In dementia care, the user-friendly robot supports person-centered care through tailored social interaction. Both family members and staff express enthusiasm for video calls as an option and see its potential for future use.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".