Humanoid robot intervention vs. treatment as usual for loneliness in long-term care homes: Study protocol for a pilot randomized controlled trial
Bibliographic record
Abstract
Background: Loneliness affects up to 42% of long-term care residents and is associated with poor health outcomes. Humanoid robot interventions hold promise for reducing loneliness and decreasing barriers to social interaction in long-term care settings, such as the current COVID-19 safety measures in many countries, limited mobility, and poor health. We present a protocol describing an assessor-blinded randomized controlled trial comparing the effects of a humanoid robot intervention to treatment as usual, on loneliness and mental health outcomes in long-term care residents. Methods: = 74) older adults experiencing loneliness in 3 long-term care homes will be randomized 1:1 to an 8-week, twice a week social intervention with the Grace humanoid robot vs. a treatment as usual active control. We will assess change (baseline to week 8) in (1) loneliness (primary outcome), (2) depression severity, and (3) stress (secondary outcomes), as well as (4) other exploratory outcomes: anxiety, quality of life and reduction in acute healthcare utilization. We will also assess the feasibility and acceptability of the intervention using qualitative methods. Discussion: The proposed study will assess the effects of a social robot on loneliness and other mental health outcomes, as well as the feasibility of the intervention in older adults living in long-term care settings. Trial registration: NCT05423899.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 0.009 |
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".