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Record W2986352096 · doi:10.1093/geroni/igz038.701

EXPLORING THE PERCEPTION OF PATIENTS WITH DEMENTIA ABOUT A SOCIAL ROBOT PARO IN A HOSPITAL SETTING

2019· article· en· W2986352096 on OpenAlexaff
Lillian Hung, Mario Gregorio, Jim Mann, Neil Horne, Christine Wallsworth, Annette Berndt, Habib Chaudhury

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDementiaPerceptionConversationThematic analysisPsychologySocial robotEthnographyApplied psychologyNursingRobotPsychotherapistQualitative researchMedicineComputer scienceCommunicationSociologyDiseaseArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract PARO, a robotic pet, was designed to provide emotional and social support for older people with dementia. This project aims to explore the perception of persons with dementia about the role of PARO in a hospital setting. Video-ethnographic methods were applied. Patient and family partners were involved in the fieldwork of data collection and analysis. We conducted conversational interviews with ten patients with dementia staying in a geriatric unit and video observations. Thematic analysis yielded three substantive themes: (a) “it’s like a buddy”, the robot helps persons with dementia to 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 contribute to providing a better understanding of the direct perspectives of patients with dementia on the use of the social robot.

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)0.000
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 score0.047
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

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

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 head0.042
GPT teacher head0.326
Teacher spread0.285 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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