Assessing Media Campaigns Linking Marijuana Non-Use with Autonomy and Aspirations: “Be Under Your Own Influence” and ONDCP’s “Above the Influence”
Two media-based interventions designed to reduce adolescent marijuana use ran concurrently from 2005 to 2009. Both interventions used similar message strategies, emphasizing marijuana’s inconsistency with personal aspirations and autonomy. “Be Under Your Own Influence” was a randomized community and school trial replicating and extending a successful earlier intervention of the same name (Slater et al. Health Education Research 21:157–167, 2006). “Above the Influence” is a continuing national television, radio, and print campaign sponsored by the Office of National Drug Control Policy (ONDCP). This study assessed the simultaneous impact of the interventions in the 20 U.S. communities. Results indicate that earlier effects of the “Be Under Your Own Influence” intervention replicated only in part and that the most plausible explanation of the weaker effects is high exposure to the similar but more extensive ONDCP “Above the Influence” national campaign. Self-reported exposure to the ONDCP campaign predicted reduced marijuana use, and analyses partially support indirect effects of the two campaigns via aspirations and autonomy.
KeywordsMarijuana Media School intervention Community intervention ONDCP
- Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structurals equation modeling: Issues and techniques (pp. 243–277). Mahwah, NJ: Erlbaum.Google Scholar
- Eddy, M. (2006). War on drugs: The National Youth Anti-Drug Media Campaign. Retrieved from http://www.usembassy.it/pdf/other/RS21490.pdf.
- Goldstein, H. A. (1990). Multilevel statistical models. New York: Oxford University Press.Google Scholar
- Hornik, R. C. (1988). Development communication. New York: Longman.Google Scholar
- Johnston, L. D., O’Malley, P. M., Bachman, J. G., & Schulenberg, J. E. (2009). Monitoring the Future national survey results on drug use, 1975–2008. Volume I: Secondary school students (NIH Publication No. 09-7402). Bethesda, MD: National Institute on Drug Abuse.Google Scholar
- Kelly, K. J., Swaim, R. C., & Wayman, J. C. (1996). The impact of a localized antidrug media campaign on targeted variables associated with adolescent drug use. Journal of Public Policy & Marketing, 15, 238–251.Google Scholar
- Kleinbaum, D. G., Kupper, L. L., Muller, K. E., & Nizam, A. (1998). Applied regression analysis and other multivariable methods (3rd ed.). London: Duxbury Press.Google Scholar
- Substance Abuse and Mental Health Administration (2010). Table 4.2B. Retrieved from http://www.oas.samhsa.gov/NSDUH/2K8NSDUH/tabs/Sect4peTabs1to16.htm Accessed January 13, 2010.
- Office of National Drug Control Policy. (1998). The National Youth Anti-Drug Media Campaign: Communication strategy statement [Brochure]. Washington, DC: Author.Google Scholar
- Shapiro, M. A. (1994). Signal detection measures of recognition memory. In A. Lang (Ed.), Measuring psychological responses to the media (pp. 133–148). Mahwah, CA: Erlbaum.Google Scholar
- Slater, M. D., Kelly, K. J., Edwards, R. W., Plested, B. A., Thurman, P. J., Keefe, T. J., et al. (2006). Combining in-school social marketing and participatory, community-based media efforts: Reducing marijuana and alcohol uptake among younger adolescents. Health Education Research, 21, 157–167.CrossRefPubMedGoogle Scholar
- Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage.Google Scholar
- Sobel, M. E. (1982). Asymptotic intervals for indirect effects in structural equation models. In S. Leinhart (Ed.), Sociological methodology (pp. 290–312). San Francisco, NJ: Jossey-Bass.Google Scholar
- White, T. (2008, May). Innovative analytic approaches to measure the impact of a drug prevention social marketing campaign. Paper presented at the International Communication Association, Health Communication Division, Montreal, Canada.Google Scholar
- Wothke, W. (2000). Longitudinal and multi-group modeling with missing data. In T. D. Little, K. U. Schnabel, & J. Baumert (Eds.), Modeling longitudinal and multiple group data: Practical issues, applied approaches and specific examples (p. 219–240). Mahwah, NJ: Erlbaum.Google Scholar