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dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorMACALLI, Melissa
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorNAVARRO, Marie
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorORRI, Massimiliano
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDAUBECH-TOURNIER, Marie
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorTHIEBAUT, Rodolphe
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorCOTE, Sylvana M.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorTZOURIO, Christophe
dc.date.accessioned2021-08-20T09:16:03Z
dc.date.available2021-08-20T09:16:03Z
dc.date.issued2021-06-15
dc.identifier.issn2045-2322en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/110176
dc.description.abstractEnSuicidal thoughts and behaviours are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviours among college students within one-year of baseline assessment. We used data collected in 2013-2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positive predictive value. At follow-up, 17.4% of girls and 16.8% of boys reported suicidal thoughts and behaviours. The models achieved good predictive performance: AUC, 0.8; sensitivity, 79% for girls, 81% for boys; and positive predictive value, 40% for girls and 36% for boys. Among the 70 potential predictors, four showed the highest predictive power: 12-month suicidal thoughts, trait anxiety, depression symptoms, and self-esteem. We identified a parsimonious set of mental health indicators that accurately predicted one-year suicidal thoughts and behaviours in a community sample of college students.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enPsychology
dc.subject.enRisk factors
dc.title.enA machine learning approach for predicting suicidal thoughts and behaviours among college students
dc.typeArticle de revueen_US
dc.identifier.doi10.1038/s41598-021-90728-zen_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed34131161en_US
dc.description.sponsorshipEuropeProgram Initiative d’Excellenceen_US
bordeaux.journalScientific Reportsen_US
bordeaux.page11363en_US
bordeaux.volume11en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue1en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamHEALTHY_BPHen_US
bordeaux.teamSISTM_BPH
bordeaux.teamPharmacoEpi-Drugsen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.identifier.funderIDConseil Régional Aquitaineen_US
bordeaux.identifier.funderIDUniversité de Bordeauxen_US
bordeaux.identifier.funderIDInstitut National Du Canceren_US
bordeaux.identifier.funderIDFondation pour la Recherche Médicaleen_US
hal.identifierhal-03323054
hal.version1
hal.date.transferred2021-08-20T09:16:08Z
hal.exporttrue
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