LOVOT ROBOT AS COMPANIONS FOR OLDER ADULTS IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract This exploratory, mixed-methods study explores how older adults living in Canadian Long-Term Care (LTC) homes experience and perceive LOVOT, an AI-driven social robot from Japan. It is an extended arm of a mixed-methods, three-country study conducted in Singapore, Hong Kong, and Canada. Our Canadian sample consists of 20 older adults and 40 interdisciplinary staff, and 10 leadership team members. The participants join four weekly sessions of interaction with LOVOT. In the quantitative portion of the study, questionnaires are administered before and after interaction with LOVOT to assess participants’ experiences of the LOVOT robot. The qualitative portion consists of individual conversational interviews with older adults and focus groups with the LTC staff and leadership. We use thematic analysis to guide our initial conceptual framework, and later use both Chi-square tests and content analysis for our quantitative and qualitative data. This study demonstrates (1) the experiences and perceptions of older adults and their family members regarding their interactions with the LOVOT robot, and (2) the LTC staff and leadership perceptions on having the LOVOT robot in LTC. The study offers insights into the potential role of social robots in LTC homes across eastern and western countries.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".