Abstract
Background: Despite modest reductions in alcohol use among college students, drinking-related harms continue to be prevalent. Group-delivered programs have had little impact on drinking except for experiential expectancy challenge interventions that are impractical because they rely on alcohol administration. Expectancy Challenge Alcohol Literacy Curriculum (ECALC), however, offers a non-experiential alternative suitable for widespread implementation for universal, selective, or indicated prevention. Objectives: ECALC has been effective with mandated students, fraternity members, and small classes of 30 or fewer first-year college students. Larger universities, however, typically have classes with 100 students or more, and ECALC has not yet been tested with groups of this size. To fill this gap, we conducted a group randomized trial in which five class sections with over 100 college students received either ECALC or an attention-matched control presentation and completed follow-up at four weeks. Results: ECALC was associated with significant changes on six subscales of the Comprehensive Effects of Alcohol Scale (CEOA), post-intervention expectancies predicted drinking at four-week follow-up, and there were significant expectancy differences between groups. Compared to the control group, students who received ECALC demonstrated significant expectancy changes and reported less alcohol use at follow-up. Conclusions: Findings suggest ECALC is an effective, single session group-delivered intervention program that can be successfully implemented in large classes.
Disclosure of interest
Preparation of this manuscript was supported by a grant from the National Institute on Alcohol Abuse and Alcoholism (1R15AA026420-01A1).
Data availability
Data will be made available through Open Science Framework (OSF).
Notes
1 Due to the high level of missing data at four-week follow-up, we explored alternative approaches to analyzing the data. First, we restricted the analysis to only those who completed follow-up. In this model, parameter estimates were essentially identical to the full sample, indirect effects remained significant, and the model fit well: χ2(80) = 139.83, p < .001, CFI = .97, RMSEA = .05, SRMR = .07. Next, we used multiple imputation from 100 imputed datasets to generate average parameter estimates for the full sample. In this analysis, all parameter estimates were again consistent with the original model, analyzed using FIML, and model fit was adequate: χ2(80) = 392.28, p < .001, CFI = .93, RMSEA = .08, SRMR = .08 Thus, we opted to retain the original model with FIML. All other analyses are available from the corresponding author.