MétaCan
Menu
Back to cohort

Osteoarthritis Depressive, Loneliness and Social Isolation in Later Life and the Robotic Companion

2025· article· en· W4413875106 on OpenAlexaff
Ray Marks

Bibliographic record

VenueJournal Of Aging Research And Healthcare · 2025
Typearticle
Languageen
FieldPsychology
TopicSocial Robot Interaction and HRI
Canadian institutionsOsteoporosis Canada
Fundersnot available
KeywordsLonelinessSocial isolationIsolation (microbiology)PsychologyGerontologyOsteoarthritisClinical psychologyMedicinePsychiatryBiologyAlternative medicinePathologyBioinformatics

Abstract

fetched live from OpenAlex

Background Older adults with disabling osteoarthritis may be severely impacted by negative emotions and pain, especially if they feel isolated. Review Aims 1) To summarize the research base concerning the presence of depression in older adults suffering from osteoarthritis; 2) To examine the degree to which mitigating loneliness is desirable in this regard and may be helped by one of the many emergent robotic social devices offering companionship; and 3) To provide directives for professionals who work or are likely to work with this population in the future. Methods Reviewed were current publications detailing some aspect of osteoarthritis in the older adult, depression, emergent loneliness and social isolation, and the role and impact of robotic personal ‘friends’ in this realm. Results Collectively, these data reveal efforts to reduce and mitigate different degrees of depression in older adult osteoarthritis cases are needed and that social robots may help quell isolation. Implication Those older adults with osteoarthritis suffering from depression and emergent loneliness and social isolation may benefit from robotic human or pet like contacts and interactions regardless of cause and overall health status, but the key is still loneliness prevention.

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 score0.236
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.097
GPT teacher head0.471
Teacher spread0.374 · 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal Of Aging Research And HealthcareSame topicSocial Robot Interaction and HRIFrench-language works237,207