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Record W4200448259 · doi:10.1093/geroni/igab046.1025

Comfort and Data Sharing With Artificial Companion Robots Among an Online Cohort

2021· article· en· W4200448259 on OpenAlexaff
Clara Berridge, Yuanjin Zhou, Julie M. Robillard, Nora Mattek, Sarah Gothard, Jeffrey Kaye

Bibliographic record

VenueInnovation in Aging · 2021
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSpouseRobotBivariate analysisPsychologyCohortComputer scienceSocial psychologyApplied psychologyDemographyArtificial intelligenceStatisticsMachine learningMathematicsSociology

Abstract

fetched live from OpenAlex

Abstract Results from a June 2020 survey on comfort with two forms of artificial companion (AC) robots in normal compared with pandemic times will be presented. 1,082 adults age 21-92 (mean 64) completed the online survey for a response rate of 45%. Significantly greater comfort is reported with small AC robots relative to larger human-shaped robots in both normal and pandemic times. In bivariate and adjusted models, younger age and male gender were most commonly associated with greater comfort with AC robots. Most participants (68.7%) did not think an AC robot would make them feel less lonely. About half (52.8 %) of the participants reported that they probably or definitely would want their facial expressions to be read, while a minority (15.0%) were at least somewhat comfortable with AC robots recording their conversations. The most common person participants wanted these data types shared with is themselves, a spouse/partner, and medical provider.

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 score0.031
Threshold uncertainty score0.358

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 head0.195
GPT teacher head0.432
Teacher spread0.237 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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