Evidence map›Paper›PMID 40936040›Full record

ArticleEuropean child & adolescent psychiatry2026

Prevalence and risk factors of suicidal ideation amongst unaccompanied young refugees: a machine learning approach.

Jacob Keller, Jenny Eglinsky, Maike Garbade, Elisa Pfeiffer, Paul L Plener, Rita Rosner, Thorsten Sukale, Cedric Sachser

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In one paragraph

Article in European child & adolescent psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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.

Jacob KellerClinical Child and Adolescent Psychology, Institute of Psychology, University of Bamberg, Kapuzinerstraße 32, 96047, Bamberg, Germany. jacob.keller@uni-bamberg.de.ORCID http://orcid.org/0000-0001-6694-5507
Jenny EglinskyClinical Child and Adolescent Psychology, Institute of Psychology, University of Bamberg, Kapuzinerstraße 32, 96047, Bamberg, Germany.ORCID http://orcid.org/0009-0003-5417-7696
Maike GarbadeDepartment of Clinical Psychology and Child and Adolescent Psychotherapy, Catholic University Eichstätt-Ingolstadt, Eichstätt, Germany.ORCID http://orcid.org/0000-0002-4585-7708
Elisa PfeifferDepartment of Child- and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ulm University, Ulm, Germany.ORCID http://orcid.org/0000-0002-9742-3004
Paul L PlenerDepartment of Child- and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ulm University, Ulm, Germany.ORCID http://orcid.org/0000-0003-4333-1494
Rita RosnerDepartment of Psychology, Catholic University Eichstätt-Ingolstadt, Eichstätt, Germany.ORCID http://orcid.org/0000-0002-7960-8398
Thorsten SukalePraxis Sukale & Markthaler, Krumbach, Germany.
Cedric SachserClinical Child and Adolescent Psychology, Institute of Psychology, University of Bamberg, Kapuzinerstraße 32, 96047, Bamberg, Germany.ORCID http://orcid.org/0000-0002-9353-7936

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSuicidality is a major public health concern worldwide. Evidence on the prevalence and risk factors of suicidality amongst unaccompanied young refugees (UYRs), a population already at risk for mental health disorders, is scarce.

methodsGiven the complexity of individual risk factor constellations influencing suicidality, machine learning (ML) methods offer a statistical approach that can detect complex relations within the data. Four ML classifiers, (logistic regression (LR), random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGB)) were trained on a dataset of n = 623 UYRs (M

resultsThe prevalence of past-week suicidal ideation in the combined sample of N = 717 was 18.13%. All classifiers yielded good predictive performance (accuracy 0.734-0.840, sensitivity 0.857, AUC 0.853-0.880). The most relevant features were past suicide attempts, PTSS and depressive symptoms as risk factors, and having a living mother as protective factor.

conclusionsSuicidal ideation is prevalent amongst UYRs, and using ML approaches, the classifiers were able to classify roughly 85% of the cases with suicidal ideation in the past week correctly as suicidal. Building on the findings of this study, screening for suicidality could be further improved by implementing ML classifiers in the assessment to highlight potential at risk cases early, and suitable interventions be developed.

Indexed as

Machine LearningRefugeesStress Disorders, Post-TraumaticSuicidal IdeationAdolescentChildDepressionFemaleHumansMalePrevalenceRisk FactorsSuicide, AttemptedYoung AdultMachine learningPrevalenceRisk factorsSuicidal ideationUnaccompanied young refugees

Identifiers

PMID40936040
PMCPMC12957002

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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.