Mostrar el registro sencillo del ítem

Innovative Identification of Substance Use Predictors: Machine Learning in a National Sample of Mexican Children

dc.creatorVázquez, Alejandro L.es_ES
dc.creatorDomenech Rodríguez, Melanie M.es_ES
dc.creatorBarret, Tayson S.es_ES
dc.creatorSchwartz, Sarahes_ES
dc.creatorAmador Buenabad, Nancy G.es_ES
dc.creatorBustos, Marycarmenes_ES
dc.creatorGutierrez, Marilúes_ES
dc.creatorVillatoro Velázquez, Jorge A.es_ES
dc.date2020
dc.date.accessioned2023-09-25T16:10:17Z
dc.date.available2023-09-25T16:10:17Z
dc.date.issued2020
dc.identifierJC41DIEP20es_ES
dc.identifier.issn1389-4986
dc.identifier.urihttp://repositorio.inprf.gob.mx/handle/123456789/7770
dc.identifier.urihttps://doi.org/10.1007/s11121-020-01089-4
dc.descriptionMachine learning provides a method of identifying factors that discriminate between substance users and non-users potentially improving our ability to match need with available prevention services within context with limited resources. Our aim was to utilize machine learning to identify high impact factors that best discriminate between substance users and non-users among a national sample (N = 52,171) of Mexican children (i.e., 5th, 6th grade; Mage = 10.40, SDage = 0.82). Participants reported information on individual (e.g., gender, grade, religiosity, sensation seeking, self-esteem, perceived risk of substance use) and socio-ecological (e.g., neighborhood quality, community type, peer influences, parenting) factors, and lifetime substance use (i.e., alcohol, tobacco, marijuana, inhalant). Findings suggest that best friend and father illicit substance use (i.e., drugs other than tobacco or alcohol) and respondent sex (i.e., boys) were consistent and important discriminator between children who tried substances and those that did not. Friend cigarette use was a strong predictor of lifetime use of alcohol, tobacco, and marijuana. Friend alcohol use was specifically predictive of lifetime alcohol and tobacco use. Perceived danger of engaging in frequent alcohol and inhalant use predicted lifetime alcohol and inhalant use. Overall, findings suggest that best friend and father illicit substance use and respondent sex appear to be high impact screening questions associated with substance initiation during Mexican childhood. Further research is needed to assess the feasibility of automated risk screening and intervention delivery utilizing machine learninges_ES
dc.formatPDFes_ES
dc.language.isoenges_ES
dc.publisherKluwer Academic/Plenum Publisherses_ES
dc.relation21(2):171-181
dc.rightsAcceso Cerradoes_ES
dc.titleInnovative Identification of Substance Use Predictors: Machine Learning in a National Sample of Mexican Childrenes_ES
dc.typeArtículoes_ES
dc.contributor.affiliationDepartment of Psychology, Utah State University, Logan, USA.
dc.contributor.emailameth@imp.edu.mx (Jorge A. Villatoro Velázquez)
dc.relation.jnabreviadoPREV SCI
dc.relation.journalPrevention Science
dc.identifier.placeEstados Unidos
dc.date.published2020
dc.identifier.organizacionInstituto Nacional de Psiquiatría Ramón de la Fuente Muñiz
dc.identifier.eissn1573-6695
dc.identifier.doi10.1007/s11121-020-01089-4
dc.subject.kwChildren
dc.subject.kwMachine learning
dc.subject.kwMexico
dc.subject.kwPrevention
dc.subject.kwRisk factors
dc.subject.kwSubstance use


Ficheros en el ítem

FicherosTamañoFormatoVer

No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem