Evaluating a Social Robot Reception Service in an Italian Geriatric Hospital (HOSPER Study): Protocol for a Usability Study
Bibliographic record
Abstract
Background: Humanoid robots are being used more frequently in health care: in geriatrics and pediatrics, robots such as "Pepper" and "Nao" have been shown to enhance communication, emotional well-being, and patient engagement. During the pandemic, their role expanded, for example, to include remote monitoring and reducing the risk of infection. Given these applications, our study examines the integration of 2 advanced humanoid robots, Pepper and Nao, within the reception area of a geriatric hospital. Objective: The study aims to develop a reception service directed to enhance the quality of the IRCCS Istituto Nazionale di Riposo e Cura per Anziani (INRCA), Ancona, Italy. The primary end point of the study is the assessment of the perceived quality of an innovative reception service. Second, the study aims at evaluating the usability, human-robot interaction, user experience, and technical performance of the solution. Methods: The HOSPER (analysis of the quality of reception services using robotic platforms, Nao and Pepper) study is a feasibility pilot project coordinated by IRCCS INRCA. During the trial, 200 older adults using services at IRCCS INRCA in Ancona will be enrolled. Two humanoid robots, Nao and Pepper, which are already available at the hospital, will be used to support reception and orientation services. Data collection will include general demographics, cognitive and functional status, usability measures, perceived service quality, and human-robot interaction indicators. Results: The recruitment and data collection started in January 2025 and will be concluded by December 2025. Results regarding the feasibility of a robotic-based reception service in a geriatric hospital will be published in 2027. Conclusions: The HOSPER study aims to assess the feasibility of an innovative robot-mediated reception service in a geriatric hospital, with specific focus on recruitment feasibility, completion rates, user acceptability, and perceived service quality. In fact, the usability and user experience evaluation will demonstrate the feasibility of the approach and provide indications for future developments and improvements.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".