A user-study with Tangy the Bingo facilitating robot and long-term care residents
Bibliographic record
Abstract
Cognitive decline among the elderly decreases their independence and quality of life. Promoting engagement in recreational activities can help reduce this decline as such activities can provide both social and cognitive stimulation. For example, Bingo is a popular recreational activity in long-term care (LTC) facilities. However, activities such as Bingo have significant time and personnel requirements, and are becoming increasingly difficult to facilitate due to the current LTC staff shortages and an increasing demand for other LTC services. To address this problem, our research focuses on the development of the autonomous socially assistive robot Tangy which is being designed to facilitate needed multi-user recreational activities. In this paper, we present a pilot study conducted with Tangy facilitating multiple Bingo sessions with groups of elderly residents at a LTC facility. The study results showed that Tangy was able to autonomously and effectively facilitate Bingo games in real interaction settings by determining its appropriate assistive behaviors. Residents also had high compliance and engagement rates with respect to Tangy and the Bingo games. A post-interaction questionnaire showed that they enjoyed playing Bingo with Tangy, liked Tangy's socially interactive attributes, and would interact with it again 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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".