MétaCan
Menu
Back to cohort
Record W2089888660 · doi:10.1017/s0714980811000663

Social Commitment Robots and Dementia

2012· article· fr· W2089888660 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

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2012
Typearticle
Languagefr
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsDementiaAggressionPsychologyPopulationGerontologyDiseaseValue (mathematics)Quality of life (healthcare)PsychiatryMedicinePsychotherapistComputer science

Abstract

fetched live from OpenAlex

In 2010, approximately 500,000 Canadians suffered from a dementia-related illness. The number of sufferers is estimated to double in about 25 years. Due to this growing demographic, dementia (most frequently caused by Alzheimer's disease) will increasingly have a significant impact on our aging community and their caregivers. Dementia is associated with challenging behaviours such as agitation, wandering, and aggression. Care providers must find innovative strategies that facilitate the quality of life for this population; moreover, such strategies must value the individual person. Social commitment robots - designed specifically with communication and therapeutic purposes - provide one means towards attaining this goal. This paper describes a study in which Paro (a robotic baby harp seal) was used as part of a summer training program for students. Preliminary conclusions suggest that the integration of social commitment robots may be clinically valuable for older, agitated persons living with dementia in long-term care settings.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.032
GPT teacher head0.301
Teacher spread0.269 · 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