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Record W2784179630 · doi:10.1109/iris.2017.8250117

The robot Tangy facilitating Trivia games: A team-based user-study with long-term care residents

2017· article· en· W2784179630 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyApplied psychologyPsychological interventionRobotCognitionHuman–computer interactionComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Robot facilitated cognitive interventions for older adults have mainly focused on one-on-one interactions or with groups of people each individually playing. In this paper, we present the design of the socially assistive robot, Tangy, for autonomously facilitating the team-based cognitively stimulating activity of Trivia with older adults which encourages users to interact with each other. A pilot study at a local long-term care facility with older adult residents demonstrated that Tangy could successfully facilitate Trivia games. In general, the participants were engaged in the activity, complied with the robot's requests, and had positive attitudes towards Tangy during the games. The Trivia game scenario also promoted cooperation and interactions between teammates. Furthermore, we compared the results in this study with the results of our previous study on the individually played game of Bingo. The comparison results showed that participants complied with the robot and were engaged during both activities, however, the team-based Trivia had higher levels of engagement and player interaction.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.045
GPT teacher head0.401
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations26
Published2017
Admission routes1
Has abstractyes

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