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Adverse Childhood Experiences and Social Determinants of Mental Health as Predictors of Adult Depression: A Machine Learning Approach.

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Title: Adverse Childhood Experiences and Social Determinants of Mental Health as Predictors of Adult Depression: A Machine Learning Approach.
Authors: Han, Eunae, Shin, Jihoon
Source: Journal of Counseling & Development (John Wiley & Sons, Inc.). Apr2026, Vol. 104 Issue 2, p268-279. 12p.
Subjects: Mental illness risk factors, Competency assessment (Law), Mental depression risk factors, Risk assessment, Boosting algorithms, Health services accessibility, Sexual orientation, Cultural awareness, Random forest algorithms, Medical care use, Social determinants of health, Income, Receiver operating characteristic curves, Sex distribution, Unemployment, Food security, Logistic regression analysis, Probability theory, Interviewing, Loneliness, Family history (Medicine), Descriptive statistics, Race, Surveys, Emotional trauma, Need (Psychology), Machine learning, Counseling, Housing, Discrimination (Sociology), Sociodemographic factors, Decision trees, Accuracy, Social support, Data analysis software, Adverse childhood experiences, Educational attainment, Neighborhood characteristics, Algorithms, Predictive validity, Sensitivity & specificity (Statistics), Evaluation, Adults
Abstract: This study applied machine learning (ML) models to the 2023 Behavioral Risk Factor Surveillance System (BRFSS) dataset, a nationally representative and state‐based survey conducted annually by the Centers for Disease Control and Prevention (CDC), to examine how adverse childhood experiences (ACEs) and social determinants of mental health (SDMH) predict adult depressive disorders. Among ML models, eXtreme Gradient Boosting (XGBoost) achieved the strongest performance, and we identified key predictors, including family history of mental illness, sex, total ACE score, loneliness, unemployment, and healthcare barriers. Subgroup analyses revealed variation across racial groups, showing the need for culturally responsive approaches. We discuss the utility of ML for advancing early identification, trauma‐informed counseling practice, and equity‐focused prevention strategies. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Counseling & Development (John Wiley & Sons, Inc.) is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: This study applied machine learning (ML) models to the 2023 Behavioral Risk Factor Surveillance System (BRFSS) dataset, a nationally representative and state‐based survey conducted annually by the Centers for Disease Control and Prevention (CDC), to examine how adverse childhood experiences (ACEs) and social determinants of mental health (SDMH) predict adult depressive disorders. Among ML models, eXtreme Gradient Boosting (XGBoost) achieved the strongest performance, and we identified key predictors, including family history of mental illness, sex, total ACE score, loneliness, unemployment, and healthcare barriers. Subgroup analyses revealed variation across racial groups, showing the need for culturally responsive approaches. We discuss the utility of ML for advancing early identification, trauma‐informed counseling practice, and equity‐focused prevention strategies. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Counseling & Development (John Wiley & Sons, Inc.) is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=192205412
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/jcad.70029
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 268
    Subjects:
      – SubjectFull: Mental illness risk factors
        Type: general
      – SubjectFull: Competency assessment (Law)
        Type: general
      – SubjectFull: Mental depression risk factors
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Boosting algorithms
        Type: general
      – SubjectFull: Health services accessibility
        Type: general
      – SubjectFull: Sexual orientation
        Type: general
      – SubjectFull: Cultural awareness
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Medical care use
        Type: general
      – SubjectFull: Social determinants of health
        Type: general
      – SubjectFull: Income
        Type: general
      – SubjectFull: Receiver operating characteristic curves
        Type: general
      – SubjectFull: Sex distribution
        Type: general
      – SubjectFull: Unemployment
        Type: general
      – SubjectFull: Food security
        Type: general
      – SubjectFull: Logistic regression analysis
        Type: general
      – SubjectFull: Probability theory
        Type: general
      – SubjectFull: Interviewing
        Type: general
      – SubjectFull: Loneliness
        Type: general
      – SubjectFull: Family history (Medicine)
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Race
        Type: general
      – SubjectFull: Surveys
        Type: general
      – SubjectFull: Emotional trauma
        Type: general
      – SubjectFull: Need (Psychology)
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Counseling
        Type: general
      – SubjectFull: Housing
        Type: general
      – SubjectFull: Discrimination (Sociology)
        Type: general
      – SubjectFull: Sociodemographic factors
        Type: general
      – SubjectFull: Decision trees
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Social support
        Type: general
      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Adverse childhood experiences
        Type: general
      – SubjectFull: Educational attainment
        Type: general
      – SubjectFull: Neighborhood characteristics
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Predictive validity
        Type: general
      – SubjectFull: Sensitivity & specificity (Statistics)
        Type: general
      – SubjectFull: Evaluation
        Type: general
      – SubjectFull: Adults
        Type: general
    Titles:
      – TitleFull: Adverse Childhood Experiences and Social Determinants of Mental Health as Predictors of Adult Depression: A Machine Learning Approach.
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            NameFull: Han, Eunae
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            NameFull: Shin, Jihoon
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            – D: 01
              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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            – TitleFull: Journal of Counseling & Development (John Wiley & Sons, Inc.)
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