Evidence map›Paper›PMID 40231302›Full record

ArticleFrontiers in robotics and AI2025

Trauma-informed care approach in developing companion robots: a preliminary observational study.

Keren Mazuz, Ryuji Yamazaki

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Keren MazuzThe School of Management, Hadassah Academic College, Jerusalem, Israel.
Ryuji YamazakiFaculty of Glocal Policy Management and Communication, Yamanashi Prefectural University, Kofu, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study explores the integration of Trauma-Informed Care (TIC) principles into the development of companion robots for elderly trauma survivors, particularly those with Post-Traumatic Stress Disorder (PTSD). The psychological effects of trauma, especially in aging populations, can complicate mental and physical health, highlighting the need for tailored technological solutions. Methods: The research involved two focus groups with Holocaust survivors (N = 12) who engaged directly with a social robot. Discussions explored their needs, barriers, and coping strategies based on their longitudinal experiences of trauma, resilience, and aging. Transcripts were thematically analyzed, identifying key TIC-related themes-safety, trust, self-compassion, and self-efficacy-in relation to engagement with companion robots. Results: Findings revealed that trauma survivors face significant challenges in communication and technology use. Safe and trusting environments were fundamental for meaningful engagement with robots. Self-compassion and self-efficacy emerged as essential to overcoming initial barriers, indicating that TIC-informed design features could facilitate better uptake and acceptance. Discussion: The study emphasizes the importance of incorporating TIC principles to ensure these robots meet the complex needs of trauma survivors. Findings also underscore the personal histories, ongoing changes in recollecting the trauma, and the need for stable, empathetic interactions highlights the complexity of designing assistive robots for profoundly affected populations. This study contributes to digital mental health and aging technologies by emphasizing stability, empathy, and user-centered design in developing assistive robotics as a universal, scalable solution.

Indexed as

companion robotelderly trauma survivorshumanoid robothuman-robot interactionobservation studyPTSDtrauma-informed care

Identifiers

PMID40231302
PMCPMC11994888

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.