The Intersectionality of Factors Predicting Co-occurring Disorders: A Decision Tree Model.
Individuals with co-occurring psychiatric and substance use disorders (COD) face challenges, including accessing treatment, accurate diagnoses, and effective treatment for both disorders. This study aimed to develop a COD prediction model by examining the intersectionality of COD with race/ethnicity...
| Publicado en: | International Journal of Mental Health & Addiction Vol. 24; no. 2; pp. 1066 - 1090 |
|---|---|
| Autores principales: | , , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Apr2026
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=193492996&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 193492996 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 15571874 46AW jtl: International Journal of Mental Health & Addiction issn: 15571874 maglogo: N pubinfo: dt: Apr2026 vid: 24 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 193492996 10.1007/s11469-024-01358-1 ppf: 1066 ppct: 24 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1MB tig: atl: The Intersectionality of Factors Predicting Co-occurring Disorders: A Decision Tree Model. aug: au: Hong, Saahoon Kim, Hea-Won Walton, Betty Kaboi, Maryanne affil: https://ror.org/01kg8sb98 Indiana University School of Social Work, 902 W. New York Street, 46202, Indianapolis, IN, USA https://ror.org/01qe4gr33 Division of Mental Health and Addiction, Indiana Family and Social Services Administration, 46204, Indianapolis, IN, USA su: Intersectionality Substance-induced disorders Social role Mental health services Mental illness Patient participation Dual diagnosis Decision trees sug: subj: Intersectionality Substance-induced disorders Social role Mental health services Mental illness Patient participation Offices of Mental Health Practitioners (except Physicians) Residential Mental Health and Substance Abuse Facilities Psychiatric and Substance Abuse Hospitals Dual diagnosis Decision trees keyword: CHAID analysis Co-occurring disorders Psychiatric disorders Substance use disorder CHAID analysis Co-occurring disorders Psychiatric disorders Substance use disorder ab: Individuals with co-occurring psychiatric and substance use disorders (COD) face challenges, including accessing treatment, accurate diagnoses, and effective treatment for both disorders. This study aimed to develop a COD prediction model by examining the intersectionality of COD with race/ethnicity, age, gender identity, pandemic year, and behavioral health needs and strengths. Individuals aged 18 or older who participated in publicly funded behavioral health services (N = 22,629) were selected. Participants completed at least two Adult Needs and Strengths Assessments during 2019 and 2020, respectively. A chi-squared automatic interaction detection (CHAID) decision tree analysis was conducted to identify patterns that increased the likelihood of having COD. Among the decision tree analysis predictors, Involvement in Recovery emerged as the most critical factor influencing COD, with a predictor importance value (PIV) of 0.46. Other factors like Legal Involvement (PIV = 0.12), Decision-Making (PIV = 0.12), Parental/Caregiver Role (PIV = 0.11), Other Self-Harm (PIV = 0.10), and Criminal Behavior (PIV = 0.09) had progressively lower PIVs. Age, gender, race/ethnicity, and pandemic year did not show statistically significant associations with COD. The CHAID decision tree analysis provided insights into the dynamics of COD. It revealed that legal involvement played a crucial role in treatment engagement. Individuals with legal challenges were less likely to be involved in treatment. Individuals with COD displayed more complex behavioral health needs that significantly impaired their functioning compared to individuals with psychiatric disorders to inform the development of targeted interventions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|