Effectiveness and Cost-Effectiveness of Using a Social Robot in Residential Care for Individuals With Challenges in Daily Structure and Planning: Protocol for a Multiple-Baseline Single Case Trial and Health Economic Evaluation
Bibliographic record
Abstract
BACKGROUND: A substantial number of individuals in disability care experience challenges with daily structure and planning and require 24-7 support. The use of a social robot might decrease their need for support from care professionals, leading to improved well-being of individuals with disabilities and increased work engagement for care professionals. OBJECTIVE: This paper presents the research protocol for an effectiveness study and a health economic evaluation from a societal perspective on the use of a social robot by individuals experiencing challenges with daily structure and planning who are living in long-term disability care facilities in the Netherlands. METHODS: We will assess the effectiveness of social robot care in reducing the level of support provided by care professionals in a multiple-baseline single case study. In total, 30 participants will be randomly allocated to 1 of 4 clusters, determining the baseline length (2, 3, 4, or 5 weeks) of a 13-week study period, and a 2-week follow-up will be conducted 6 months after participants start using the robot. During baseline, participants will receive care as usual. After baseline, participants will use the robot as part of their care plan. For each participant, 3 to 5 personal goals will be formulated, and attainment of these goals will be evaluated weekly. A health economic evaluation from a societal perspective will be performed to assess the cost-effectiveness. RESULTS: This study was funded in July 2023. As of October 2024, we enrolled 29 participants. Data collection is planned to be finished in the third quarter of 2025. Data analysis will be performed from the second quarter of 2025. Results will be published in peer-reviewed journals and presented at international conferences in 2026. CONCLUSIONS: We will provide insights into the effectiveness and cost-effectiveness of social robot care for individuals living in Dutch residential care facilities, aimed at enabling them to live more independently, reducing pressure on Dutch care professionals in times of growing staff shortages in long-term care, and allowing care facilities to make informed decisions about implementing such a technology. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67841.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".