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Record W2986048981 · doi:10.1093/geroni/igz038.1206

IT’S MY BUDDY: EXPLORING THE PERCEPTIONS OF PEOPLE WITH DEMENTIA ABOUT THE SOCIAL ROBOT PARO IN A HOSPITAL SETTING

2019· article· en· W2986048981 on OpenAlexaffabout
Lillian Hung, Habib Chaudhury

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDementiaConversationThematic analysisPerceptionPsychologySocial robotFocus groupRobotApplied psychologyUnit (ring theory)NursingQualitative researchMedicineComputer scienceCommunicationSociologyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract New technology such as social robots opens up new opportunities in hospital settings. PARO, a robotic pet seal, was designed to provide emotional and social support for older people with dementia. This project aims to explore the perceptions of persons with dementia about PARO’s role in a hospital setting. Video-ethnographic methods were applied. We had conversational interviews with and video observations of 10 older people with dementia in the geriatric unit of a large Canadian hospital. Also, semi-structured interviews and two focus groups were conducted with 10 staff members in the local unit to gain contextual information. Thematic analysis yielded three substantive themes: (a) “it’s like a buddy”—the robot helps people with dementia uphold a sense of self in the world; (b) “it’s a conversation piece”—the baby seal facilitates social connection; and (c) “it’s all about love”—PARO transforms and humanizes the clinical setting. Our findings help provide a better understanding of the direct perspectives of patients with dementia on the use of social robots. Instead of substituting human contact, the social robot complements emotional care and supports our fundamental human need for love.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.150
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.039
GPT teacher head0.352
Teacher spread0.313 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes2
Has abstractyes

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