Analysis of posttraumatic embitterment disorders by machine learning: Could sullenness be a predictor of posttraumatic embitterment disorder?

Basit Öğe Kaydı
dc.authoridUnal, Suheyla/0000-0003-3266-6256
dc.authoridUnal, Suheyla/0000-0003-3266-6256
dc.authoridKILIC, BAHAR/0000-0001-7233-8965
dc.authorwosidUnal, Suheyla/HJH-7559-2023
dc.authorwosidUnal, Suheyla/JVO-8367-2024
dc.contributor.authorKilic, Bahar
dc.contributor.authorKarakaplan, Mustafa
dc.contributor.authorUnal, Suheyla
dc.date.accessioned2024-08-04T20:10:14Z
dc.date.available2024-08-04T20:10:14Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_US
dc.description.abstractObjective: This study aimed to determine some fundamental factors specific to posttraumatic embitterment disorder (PTED) using deep machine learning (ML) and network analysis techniques. Method: Sociodemographic data form, Buss-Perry Aggression Questionnaire, Brief Symptom Inventory (BSI), PTED Self-Rating Scale (PTED Scale), and list of stressful life events were administered to 557 people who applied to the outpatient anxiety clinic. ML method and network analysis were applied with the 33 most significant variables. Results: PIED was found in urban areas (p=0.006), individual health problems (p=0.029), early separation from their families (p=0.040), previous trauma (p=0.021), describing childhood sexual abuse (p<0.001), and those with the illness for more than 10 years (p<0.001) were detected at a higher rate than those without. The PIED score was higher in those with an anxiety disorder (p=0.043) and a personality disorder (p<0.001). Almost all life stressors were higher in the PIED group. There was a statistically significant difference between the groups in all subscales of the BSI. When the ML procedure was applied, sullenness was identified as the main symptom of PIED. The factors most associated with sullenness were well-being, hopelessness, and painful event experience. Conclusion: The higher rate of chronic trauma in the group with PTED and the detection of sullenness as the main symptom have been important data for understanding the psychopathological process.en_US
dc.identifier.doi10.14744/DAJPNS.2022.00182
dc.identifier.endpage120en_US
dc.identifier.issn1018-8681
dc.identifier.issn1309-5749
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85140442822en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage111en_US
dc.identifier.trdizinid1136973en_US
dc.identifier.urihttps://doi.org/10.14744/DAJPNS.2022.00182
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1136973
dc.identifier.urihttps://hdl.handle.net/11616/92675
dc.identifier.volume35en_US
dc.identifier.wosWOS:000813492000007en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherKare Publen_US
dc.relation.ispartofDusunen Adam-Journal of Psychiatry and Neurological Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine learningen_US
dc.subjectnetwork analysisen_US
dc.subjectposttraumatic embitterment disorderen_US
dc.subjectsullennessen_US
dc.titleAnalysis of posttraumatic embitterment disorders by machine learning: Could sullenness be a predictor of posttraumatic embitterment disorder?en_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
TR-Dizin İndeksli Yayınlar Koleksiyonu