Using machine learning to determine the shared and unique risk factors for marijuana use among child-welfare versus community adolescents

This study used machine learning (ML) to test an empirically derived set of risk factors for marijuana use. Models were built separately for child welfare (CW) and non-CW adolescents in order to compare the variables selected as important features/risk factors. Data were from a Time 4 (Mage = 18.22)...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	PloS one 2022-09, Vol.17 (9), p.e0274998
Hauptverfasser:	Negriff, Sonya, Dilkina, Bistra, Matai, Laksh, Rice, Eric
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Addictions Adolescent Adolescent development Adolescents Adults Aggression Alcohol use Biology and Life Sciences Cannabis Caregivers Child Child abuse & neglect Child development Child welfare Child Welfare - psychology Children Comparative analysis Computer and Information Sciences Drug use Drugs and youth Families & family life Health aspects Health behavior Health risks Humans Learning algorithms Longitudinal Studies Machine Learning Male Marijuana Marijuana Use - epidemiology Medicine and Health Sciences Mental health Orphans People and Places Personality Physical Sciences Postal codes Prevention Psychopathology Research and Analysis Methods Risk analysis Risk assessment Risk behavior Risk Factors Risk factors (Health) Risk taking Self esteem Social aspects Social behavior Social interactions Social Sciences Social support Substance abuse Substance use Substance-Related Disorders - epidemiology Teenagers Violence Youth
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!