Using Attitudes, Age and Gender to Estimate an Adolescent's Substance Use Risk

Social Network Methods for Assigning Students to Teams

Teachers often group students into teams to organize their classrooms and network-informed interventions hold great promise as a way to facilitate positive peer influence and promote the diffusion of intervention effects. Yet thus far, relatively little research has explored how teachers or prevention scientists can best use social network information to assign students to teams. The goal of the present study was to identify and compare seven methods that use different data sources and assignment algorithms to create teams of students. To test these methods, we used survey data from 247 5th through 8th grade students in three rural schools that assessed students' social networks, sociability, values and interests, and bonding to school. To create teams, we first identified popular students (i.e., those who received the highest number of peer nominations) who also had school bonding scores in the normal range and formed 4-person teams around them, applying different methods to assign students to teams. In all but one method, we placed at-risk students (i.e., those who had the lowest school bonding scores) in teams only during the final round of team creation. Team assignments were compared against three criteria: (1) team-level bonding to school, (2) patterns of affiliation among teammates, and (3) shared values and interests. Two methods, one that used only social network data and one that used social network data in combination with students' values and interests, yielded the most promising outcomes. The most positive results were obtained when a pruning algorithm akin to the one proposed by Girvan and Newman (2002) Proceedings of the National Academy ofSciences, 99, 7821-7826 was used to select which dyads to join as teammates; this pruning method joined more weakly linked students first, maximizing their potential to find suitable matches. These methods for team assignment hold promise for designing network-informed school-based interventions.

Piloting of the Just Say Know prevention program: a psychoeducational approach to translating the neuroscience of addiction to youth

BACKGROUND

Substance use during adolescence can have a number of negative consequences and interfere with normal brain development. Given limited time and resources, brief group- and school-based prevention programs are an efficient strategy for educating youth about the effects of substance use on health outcomes.

OBJECTIVES

To determine if a science-based, interactive substance prevention program could improve student knowledge and influence students' attitudes toward future substance use behaviors.

METHODS

The Just Say Know program was given to 1,594 middle and high school students. The facilitator engaged students in an interactive, hour-long session covering brain basics and effects of substance use. Students completed an eight-item pre- and post-knowledge-based test to measure learning outcomes along with feedback questions about youths' attitudes toward substance use and the program.

RESULTS

After the program, 94% of students reported that it provided helpful information; 92% reported it may influence their approach to substance use, with 76% specifying that they would delay or cut back on substance use. Knowledge-based test performance increased by 78%, with high schoolers displaying significantly higher scores than middle schoolers, but both showing similar improvements in scores. Students who reported higher levels of friends' substance use had smaller improvements from pre- to posttest.

CONCLUSION

Results suggest Just Say Know, a scientifically-based prevention program, is effective in increasing adolescents' program based-knowledge, has the potential to affect youths' attitudes toward substance use, and is well-received. These findings provide preliminary evidence that a cost-effective, neuroscience-informed group prevention program might reduce or delay adolescents' future substance use.

Measuring Attitudes Toward Medical and Recreational Cannabis Among Older Adults in Colorado

BACKGROUND AND OBJECTIVES

Cannabis use among older adults is on the rise. Despite growing interest in the topic, there exists a paucity of standardized measures capturing cannabis-specific attitudes among older adults. Using data from a survey of older Coloradans, we create two scales that separately measure medical and recreational cannabis attitudes. We also examine how these two attitudes relate to individual-level characteristics.

RESEARCH DESIGN AND METHODS

We assess reliability using Cronbach's alpha and item-rest correlations and perform confirmatory factor analyses to test the two attitude models. We conduct a seemingly unrelated regression estimation to assess how individual characteristics predict medical and recreational cannabis attitude scores.

RESULTS

Twelve indicators combined into two valid and reliable scales. Both scales had a three-factor structure with affect, cognition and social perception as latent dimensions. For both scales, fit indices for the three-factor model were statistically superior when compared with other models. The three-factor structure for both scales was invariant across age groups. Age, physical health, and being a caregiver differentially predicted medical and recreational cannabis attitude scores.

DISCUSSION AND IMPLICATIONS

Medical and recreational cannabis attitude scales can inform the development and evaluation of tailored interventions targeting older adult attitudes that aim to influence cannabis use behaviors. These scales also enable researchers to measure cannabis-specific attitudes among older adults more accurately and parsimoniously, which in turn can facilitate a better understanding of the complex interplay between cannabis policy, use, and attitudes.

Self-Reported Engagement in a Drug Prevention Program: Individual and Classroom Effects on Proximal and Behavioral Outcomes

Numerous studies emphasize the role of student engagement in academic learning and performance. Less known is whether engagement plays a role in drug prevention program outcomes. We examined a self-report measure of engagement as part of the All Stars Core drug prevention program evaluation, assessing its impact on target risk mechanisms and behavioral outcomes. Students completed pretests just prior to and posttests just after completing the intervention. Surveys assessed demographics, proximal intervening measures (i.e., commitments to avoid substance use and antisocial behavior, perceived lifestyle incongruence with substance use and antisocial behavior, normative beliefs about substance use and antisocial behavior, and parental attentiveness), and distal outcome measures of alcohol, cigarette use, and antisocial behaviors. A brief 6-item posttest measure including items tapping the students' perspective on the quality of teaching the program material and their level of engagement with the program was internally consistent (α = .79). Multi-level analyses positing engagement effects at both the classroom- and individual-level indicated that classroom average engagement was significantly associated with all the targeted risk mechanisms, and outcomes of antisocial behavior and alcohol use, controlling for pretest measures and classroom size. Individual student engagement relative to classroom peers was significantly associated with all posttest target risk mechanisms and behavioral outcomes. The current findings suggest that students should routinely provide assessments of engagement and perceived quality of teaching, which would improve our understanding of how prevention programs work. Teachers can improve engagement by paying attention to students when they speak in class, making the program enjoyable to participants, encouraging students to share opinions, stimulating attentiveness, being well prepared to deliver the intervention, and helping students think broadly about implications of drug prevention as it affects their lives. This type of support will ultimately engage students in ways that will enhance the likelihood that these programs will have their desired effects.

An Algorithm for Creating Virtual Controls Using Integrated and Harmonized Longitudinal Data

We introduce a strategy for creating virtual control groups-cases generated through computer algorithms that, when aggregated, may serve as experimental comparators where live controls are difficult to recruit, such as when programs are widely disseminated and randomization is not feasible. We integrated and harmonized data from eight archived longitudinal adolescent-focused data sets spanning the decades from 1980 to 2010. Collectively, these studies examined numerous psychosocial variables and assessed past 30-day alcohol, cigarette, and marijuana use. Additional treatment and control group data from two archived randomized control trials were used to test the virtual control algorithm. Both randomized controlled trials (RCTs) assessed intentions, normative beliefs, and values as well as past 30-day alcohol, cigarette, and marijuana use. We developed an algorithm that used percentile scores from the integrated data set to create age- and gender-specific latent psychosocial scores. The algorithm matched treatment case observed psychosocial scores at pretest to create a virtual control case that figuratively "matured" based on age-related changes, holding the virtual case's percentile constant. Virtual controls matched treatment case occurrence, eliminating differential attrition as a threat to validity. Virtual case substance use was estimated from the virtual case's latent psychosocial score using logistic regression coefficients derived from analyzing the treatment group. Averaging across virtual cases created group estimates of prevalence. Two criteria were established to evaluate the adequacy of virtual control cases: (1) virtual control group pretest drug prevalence rates should match those of the treatment group and (2) virtual control group patterns of drug prevalence over time should match live controls. The algorithm successfully matched pretest prevalence for both RCTs. Increases in prevalence were observed, although there were discrepancies between live and virtual control outcomes. This study provides an initial framework for creating virtual controls using a step-by-step procedure that can now be revised and validated using other prevention trial data.