An ecological examination of early adolescent e-cigarette use: A machine learning approach to understanding a health epidemic.

E-cigarette use among adolescents is a national health epidemic spreading faster than researchers can amass evidence for risk and protective factors and long-term consequences associated with use. New technologies, such as machine learning, may assist prevention programs in identifying at risk youth and potential targets for intervention before adolescents enter developmental periods where e-cigarette use escalates. The present study utilized machine learning algorithms to explore a wide array of individual and socioecological variables in relation to patterns of lifetime e-cigarette use during early adolescence (i.e., exclusive, or with tobacco cigarettes). Extant data was used from 14,346 middle school students (Mage = 12.5, SD = 1.1; 6th and 8th grades) who participated in the Utah Prevention Needs Assessment. Students self-reported their substance use behaviors and related risk and protective factors. Machine learning algorithms examined 112 individual and socioecological factors as potential classifiers of lifetime e-cigarette use outcomes. The elastic net algorithm achieved outstanding classification for lifetime exclusive (AUC = .926) and dual use (AUC = .944) on a validation test set. Six high value classifiers were identified that varied in importance by outcome: Lifetime alcohol or marijuana use, perception of e-cigarette availability and risk, school suspension(s), and perceived risk of smoking marijuana regularly. Specific classifiers were important for lifetime exclusive (parent's attitudes regarding student vaping, best friend[s] tried alcohol or marijuana) and dual use (best friend[s] smoked cigarettes, lifetime inhalant use). Our findings provide specific targets for the adaptation of existing substance use prevention programs to address early adolescent e-cigarette use.

Barriers to accessing telepsychology services questionnaire: Structure and language-based performance in a sample of Latinx caregivers.

A lack of validated measures to examine barriers to youth telepsychology services among Latinx families limits research that could identify targets for reducing mental health disparities. We developed and validated the Latinx Barriers to Accessing Telepsychology Services (Latinx BATS) questionnaire, a brief multidimensional measure for caregivers of youths. Participants included 511 Latinx caregivers of youths Ages 6-18 (English n = 275, Spanish n = 236) who completed the Latinx BATS and reported on telepsychology service utilization and youth mental health problems. Caregivers whose youths had clinically elevated mental health problems were more likely to report barriers to accessing youth telepsychology services compared to nonclinical youths. Commonly endorsed barriers included concerns that the child would be distracted and not get much benefit, and that providers would be unfamiliar with the family's culture or would not pick up on nuances and emotions. Loadings from an exploratory graph analysis returned four-factors: relational, acceptability, quality, and access concerns. Network centrality measures identified provider knowledge regarding community resources and Latinx culture as important targets for reducing barriers to youth telepsychology services. Confirmatory factor analyses were then conducted and found that the four-factor structure outperformed a single-factor solution. The four-factor structure was similar for the English and Spanish versions of the Latinx BATS, but the strength of item loadings varied across languages. Implications for the use of the Latinx BATS in research and clinical practice are discussed including specific strategies for reducing these obstacles to care among Latinx families. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cuento de nunca acabar [never-ending story]: Compounding trauma and mental health among undocumented Latinx immigrants.

Research examining the effects of traumatic events on undocumented Latinx immigrants often focuses on assessing posttraumatic stress disorder or general psychological distress, which may obscure the field's understanding of how trauma exposure impacts other common mental health disorders (e.g., anxiety, depression). This study sought to assess the cumulative, individual, and timing effects of immigration-related traumatic events on anxiety and depressive symptoms among undocumented Latinx immigrants. Participants were 253 undocumented Latinx immigrants recruited using respondent-driven sampling who reported their history of immigration-related trauma exposure and symptoms of depression and anxiety. Results suggest that cumulative immigration-related trauma was significantly associated with increases in anxiety and depressive symptoms, τ = .26. Significant positive correlations were found for cumulative trauma at each point in the immigration process (i.e., before immigration, while in transit to the United States, and while living in the United States) such that increases in the number of events were associated with higher anxiety and depressive symptom levels, τ = .11-.29. Trauma frequency differed throughout the immigration process such that some events more commonly occurred before immigration or during transit to the United States, whereas others occurred while an individual resided in the United States. Random forest algorithms uncovered differences in the relative importance of individual traumatic events in explaining the variance of depressive, R2 = .13, and anxiety symptoms, R2 = .14. The findings highlight the importance of providing trauma-informed care when treating anxiety and depression among undocumented Latinx immigrants and considering multidimensional epidemiological approaches in assessing immigration-related trauma.

High value correlates of caregiver reported counseling service need and utilization for adolescents at-risk for childhood maltreatment and neglect.

Adolescents with a history of child maltreatment experience increased risk for psychopathology that sets them on a trajectory towards a range of difficulties in adulthood. Various factors influence caregivers' decisions to seek mental health services (MHS) that could improve developmental outcomes. The present study applied a machine learning algorithm, elastic net, to a sample of 878 adolescent-caregiver dyads from the Longitudinal Studies of Child Abuse and Neglect. Analyses simultaneously examined a large number of factors to determine their ability to discriminate between caregivers who perceived a need for MHS and those who did not, as well as caregivers who utilized MHS and those who did not. Results highlight family demographics, chronic parental stressors, youth psychopathology, and exposure to recent adversities as good classifiers of caregiver perceived need for (77.6%; sensitivity = .77; specificity = .78) and utilization of (71%; sensitivity = .71; specificity = .71) adolescent MHS. Elastic net identified adolescent clinical externalizing and internalizing problems, and parental stress related to child(ren)'s behavior as high value classifiers of both outcomes. Youth living with non-kin caregivers were also significantly more likely to utilize MHS. Findings highlight the importance of assessing clinical need, stress related to child(ren)'s behavior, and caregiver kinship in understanding the likelihood that at-risk families will seek adolescent MHS.