MétaCan
Menu
Back to cohort
Record W2060479977 · doi:10.3928/00989134-20120911-02

The Use of Socially Assistive Robots for Dementia Care

2012· review· en· W2060479977 on OpenAlexaff
Julie Huschilt, Laurie Clune

Bibliographic record

VenueJournal of Gerontological Nursing · 2012
Typereview
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsFleming College
Fundersnot available
KeywordsDementiaStaffingIndependence (probability theory)NursingPsychologyQuality of life (healthcare)Quality (philosophy)MedicineGerontology

Abstract

fetched live from OpenAlex

Innovative solutions for dementia care are required to address the steady rise in adults living with dementia, lack of adequate staffing to provide high-quality dementia care, and the need for family caregivers to provide care for their loved ones in the home. This article provides an overview of the use of socially assistive robots (SARs) to offer support as therapists, companions, and educators for people living with dementia. Social, ethical, and legal challenges associated with the use of robotic technology in patient care and implications for the use of SARs by nurses are discussed. These items considered, the authors conclude that SARs should be considered as a viable way to assist people living with dementia to maintain their highest possible level of independence, enhance their quality of life, and provide support to overburdened family caregivers. Further research is needed to evaluate the merits of this technological approach in the care of adults with dementia.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.488
GPT teacher head0.520
Teacher spread0.031 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations63
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Gerontological NursingSame topicSocial Robot Interaction and HRIFrench-language works237,207