Osteoarthritis Depressive, Loneliness and Social Isolation in Later Life and the Robotic Companion
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".