Exploring fathers' psychological well‐being using supervised machine learning random forest analysis.
Objective: Guided by a family systems theoretical framework, the study reported herein explores the utility of using supervised machine learning random forest regression for understanding fathers' psychological well‐being. Background: Although fathers' psychological well‐being has not received much...
| Publicado en: | Family Relations (John Wiley & Sons, Inc.) Vol. 74; no. 3; pp. 1198 - 1216 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
John Wiley & Sons, Inc.
Jul2025
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| 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=185725893&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 185725893 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 17413729 NRNL jtl: Family Relations (John Wiley & Sons, Inc.) issn: 17413729 maglogo: N pubinfo: dt: Jul2025 vid: 74 iid: 3 pid: 52269 pub: John Wiley & Sons, Inc. artinfo: ui: 185725893 10.1111/fare.13191 ppf: 1198 ppct: 18 formats: tig: atl: Exploring fathers' psychological well‐being using supervised machine learning random forest analysis. aug: au: Ko, Kwangman Rodriguez, Matthew R. affil: Department of Counseling and Human Services, East Tennessee State University, Johnson City TN University of California Cooperative Extension, Auburn CA su: Psychological well-being Artificial intelligence Parent-child relationships Random forest algorithms Machine learning sug: subj: Psychological well-being Artificial intelligence Parent-child relationships Random forest algorithms Machine learning keyword: coparenting father involvement fathers' psychological well‐being parent–child relationships supervised machine learning random forest coparenting father involvement fathers' psychological well‐being parent–child relationships supervised machine learning random forest ab: Objective: Guided by a family systems theoretical framework, the study reported herein explores the utility of using supervised machine learning random forest regression for understanding fathers' psychological well‐being. Background: Although fathers' psychological well‐being has not received much attention, understanding how familial factors contribute to fathers' mental health will benefit fathers themselves as well as their families, given the interdependence of the family system. Supervised machine learning using a random forest regression can be useful for identifying the hierarchical relationships between factors that shape fathers' psychological well‐being. Method: The study includes 277 U.S. fathers with at least one preschool‐aged child as participants. Study variables include fathers' psychological well‐being, father involvement, parental competency, parent–child relationships, coparenting relationship quality, work and family conflict, and fathers' demographic information. Results: The supervised machine learning model was trained using a random forest regression. After tuning, the random forest regression with the training data identified parent–child relationship conflict as the most important predictor, followed by father involvement, coparenting relationships, work and family conflict, and parental competency (R2 =.62). Conclusion: This research shows the benefits of taking a supervised machine learning random forest statistical approach to increasing understanding of the complexity of factors related to fathers' psychological well‐being. Implications: To support fathers' psychological well‐being, practitioners and family educators may consider addressing familial factors such as parent–child relationship conflict, father involvement, and coparenting relationship quality within a family. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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