Evidence map›Paper›PMID 41495734›Full record

ArticleBMC psychiatry2026

The effectiveness of multilingual AI-based simulator for suicide risk assessment training in improving self-efficacy among young psychiatrists: a pilot study across twenty languages.

Zohar Elyoseph, Yossi Levi-Belz, Inbar Levkovich, Yuval Haber, Carla Maria Gramaglia, Jorge López Castroman, Hanon Cecile, Emilie Olie

Abstract read
In one paragraph

Article in BMC psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

8 authors.

Zohar Elyoseph *Faculty of Education, School of Therapy Counseling and Human Development, University of Haifa, 199 Abba Khoushy Ave. Mount Carmel, Haifa, 3498838, Israel. zohar.j.a@gmail.com.
Yossi Levi-Belz *Faculty of Education, School of Therapy Counseling and Human Development, University of Haifa, 199 Abba Khoushy Ave. Mount Carmel, Haifa, 3498838, Israel.
Inbar LevkovichTel l-Hai Academic College, Kiryat Shmona, Israel.
Yuval HaberThe PhD Program of Hermeneutics and Cultural Studies, Interdisciplinary Studies Unit, Bar-Ilan University, A Gan, Israel.
Carla Maria GramagliaPsychiatry Unit, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy.
Jorge López CastromanDepartment of Psychiatry, Radiology, Public Health, Nursing and Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain.
Hanon CecileRegional Resource Center of Old Age Psychiatry, APHP, University of Paris, Paris, France.
Emilie OlieDepartment of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSuicide represents a major public health concern. Suicide risk assessment, considered one of the most complex clinical situations in psychiatry, is crucial for prevention. However, mental health clinicians report low self-efficacy and willingness to conduct these assessments. Current training processes are predominantly theoretical, limiting opportunities to practice asking challenging questions. Furthermore, there are global challenges in achieving international standardization of training while maintaining linguistic and cultural appropriateness.

methodsThe study evaluated the SEE THE PAIN system, a Generative AI (GenAI) simulator providing skills-based suicide risk assessment training across 20 languages. Twenty-seven psychiatrists attending the European Psychiatric Association Summer School participated. Participants conducted a risk assessment of a virtual character via the simulator and completed measures of self-efficacy and willingness to treat at pre- and post-intervention points.

resultsAnalysis demonstrated significant improvements in self-efficacy (from M = 6.7/10 to M = 7.6/10, p = .001) and willingness to treat (from M = 6.5/10 to M = 7.59/10, p < .001) following the intervention. User experience ratings were notably positive, with mean scores of 7.19/10 (SD = 1.73) for future utility and 7.81/10 (SD = 1.69) for feedback quality. Improvements in self-efficacy showed a significant negative correlation with years of clinical experience (r = -.54, p = .01), suggesting particular effectiveness for early-career practitioners.

conclusionsGenAI based multilingual simulation training effectively addresses the complex challenges in suicide risk assessment training by enhancing both self-efficacy and willingness to conduct assessments. The system bridges the gap between theoretical knowledge and practical skills while providing standardized, culturally adapted training across language barriers. These findings suggest promising potential for improving global psychiatric training standards in this critical clinical skill. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

MultilingualismPsychiatristsPsychiatrySelf EfficacySimulation TrainingSuicide PreventionAdultFemaleGenerative Artificial IntelligenceHumansLanguageMalePilot ProjectsRisk AssessmentClinical self-efficacyGenAI simulatorGenerative AIMental health trainingMultilingual trainingProfessional developmentPsychiatric educationRisk assessmentSuicide preventionVirtual patient

Identifiers

PMID41495734
PMCPMC12857119

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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.