paper-with-me

Papers

Continuous Focus Groups: A Longitudinal Method for Clinical HRI in Autism Care

2026-04-20 · Ghiglino Davide, Foglino Caterina, Wykowska Agnieszka arxiv

Qualitative methods are important to use alongside quantitative methods to improve Human-Robot Interaction (HRI), yet they are often applied in static or one-off formats that cannot capture how stakeholder perspectives evolve over time. This limitation is especially evident in clinical contexts, where families and patients face heavy burdens and cannot easily participate in repeated research encounters. To address this gap, we introduce continuous focus groups, a longitudinal and co-agential method designed to sustain dialogue with assistive care professionals working with children with autism spectrum disorder (ASD). Three focus groups were organized across successive phases of a robot-assisted therapeutic protocol, enabling participants to revisit and refine earlier views as the intervention progressed. Results show that continuity fostered trust, supported the integration of tacit clinical expertise into design decisions, and functioned as an ethical safeguard by allowing participants to renegotiate involvement and surface new concerns. By bridging the therapeutic iteration of families, children, and clinicians with the research-design iteration of researchers and developers, continuous focus groups provide a methodological contribution that is both feasible in practice and rigorous in design. Beyond autism care, this approach offers a transferable framework for advancing qualitative research in HRI, particularly in sensitive domains where direct user participation is limited and continuity is essential.

📄 PDF Abstract BibTeX arXiv:2604.18197

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Disease2Vec: Representing Alzheimer's Progression via Disease Embedding Tree

2021-02-13 · Lu Zhang, Li Wang, Tianming Liu, Dajiang Zhu

For decades, a variety of predictive approaches have been proposed and evaluated in terms of their prediction capability for Alzheimer's Disease (AD) and its precursor - mild cognitive impairment (MCI). Most of them focu…

Multi-class Classification

Deciphering autism heterogeneity: a molecular stratification approach in four mouse models

2024-03-27 · Caroline Gora, Ana Dudas, Océane Vaugrente, Lucile Drobecq 외

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in social interaction, communication, as well as restrained or stereotyped behaviors. The inherent heterogeneity withi…

Validated Synthetic Patient Generation for Small Longitudinal Cohorts: Coagulation Dynamics Across Pregnancy

2026-04-08 · Jeffrey D. Varner, Maria Cristina Bravo, Carole McBride, Thomas Orfeo 외 arxiv

Small longitudinal clinical cohorts, common in maternal health, rare diseases, and early-phase trials, limit computational modeling: too few patients to train reliable models, yet too costly and slow to expand through ad…

Evaluation of data imputation strategies in complex, deeply-phenotyped data sets: the case of the EU-AIMS Longitudinal European Autism Project

2022-01-20 · A. Llera, M. Brammer, B. Oakley, J. Tillmann 외

An increasing number of large-scale multi-modal research initiatives has been conducted in the typically developing population, as well as in psychiatric cohorts. Missing data is a common problem in such datasets due to …

ImputationMissing Valuesregression

Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum

2026-08-19 · Yingying Zhang, Kun Zhao, Guodong Liu, Qi Huang 외 arxiv

Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remain limited to discrete diagnosis or clinical score prediction from cros…