Acceptance of Drug Use Mediates Future Hard Drug Use Among At-Risk Adolescent Marijuana, Tobacco, and Alcohol Users

Cappelli, Christopher; Ames, Susan L.; Xie, Bin; Pike, James Russell; Stacy, Alan W.

doi:10.1007/s11121-020-01165-9

Published: 15 September 2020

Acceptance of Drug Use Mediates Future Hard Drug Use Among At-Risk Adolescent Marijuana, Tobacco, and Alcohol Users

Christopher Cappelli ORCID: orcid.org/0000-0003-1886-749X¹,
Susan L. Ames²,
Bin Xie²,
James Russell Pike² &
Alan W. Stacy²

Prevention Science volume 22, pages 545–554 (2021)Cite this article

669 Accesses
Metrics details

Abstract

Alternative high school (AHS) students typically report higher levels of alcohol and other drug use compared to students attending traditional high schools. Greater use of such drugs as heroin, methamphetamines, and cocaine in this at-risk population may be driven, in part, by a greater latitude of acceptance toward substance use in general, which may accelerate the transition from gateway drugs to hard drugs. Seven hundred seventy-seven adolescents (mean age 16.6; 56% female) were recruited from alternative high schools throughout Southern California. To understand the factors that may lead AHS students to use hard drugs, a model was tested in order to determine if AHS students’ latitude of acceptance toward substance use was a mediator between the relationship of past use of gateway drugs and future use of hard drugs. Latitude of acceptance was found to be a statistically significant mediator of future hard drug use (b = 0.03, 95% confidence intervals = 0.01 to 0.05) among gateway drug users. An individual’s latitude of acceptance to various drug use behaviors may be consistent with societal norms. However, after exposure to, or use of, gateway drugs, attitudes that are more permissive toward hard drug use may be encountered, the acceptance of hard drugs may expand, and the use of hard drugs may escalate. Interventions designed to reduce the use of hard drugs among at-risk youth may be more persuasive by crafting messages that are within the latitude of acceptance of the target population and prevent the acceptance of hard drug use.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price includes VAT (USA)
Tax calculation will be finalised during checkout.

Notes

Four hundred forty-seven participants completed the Time 2 assessment and 449 participants completed the Time 3 assessment.
Results are substantively similar when analysis with imputed dataset excludes students who reported hard drug use at baseline (BootCI, 0.009–0.015).

References

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control (pp. 11–39). Berlin: Springer.
Chapter Google Scholar
Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84, 888–918. https://doi.org/10.1037/0033-2909.84.5.888.
Article Google Scholar
Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior: Prentice-Hall.
Ajzen, I., & Fishbein, M. (2000). Attitudes and the attitude-behavior relation: Reasoned and automatic processes. European Review of Social Psychology, 11, 1–33. https://doi.org/10.1080/14792779943000116.
Article Google Scholar
Allen, J. P., Chango, J., Szwedo, D., Schad, M., & Marston, E. (2012). Predictors of susceptibility to peer influence regarding substance use in adolescence. Child Development, 83, 337–350. https://doi.org/10.1111/j.1467-8624.2011.01682.x.
Article PubMed Google Scholar
Allison, P. D. (2001). Missing data. Thousand Oaks: Sage University.
Google Scholar
Ames, S. L., Franken, I. H., & Coronges, K. (2006). Implicit cognition and drugs of abuse. In R. W. Wiers & A. W. Stacy (Eds.), Handbook of implicit cognition and addiction (pp. 363–378). Thousand Oaks: SAGE Publications.
Chapter Google Scholar
Ames, S. L., Grenard, J. L., Thush, C., Sussman, S., Wiers, R. W., & Stacy, A. W. (2007). Comparison of indirect assessments of association as predictors of marijuana use among at-risk adolescents. Experimental and Clinical Psychopharmacology, 15, 204–218. https://doi.org/10.1037/1064-1297.15.2.218.
Article PubMed PubMed Central Google Scholar
Ames, S.L., Grenard, J.L., Stacy, A.W., Xiao, L., He, Q., Wong, S.W., … Bechara, A. (2013). Functional imaging of implicit marijuana associations during performance on an implicit association test (IAT). Behavioural Brain Research, 256, 494–502. doi:https://doi.org/10.1016/j.bbr.2013.09.013.
Ames, S. L., Xie, B., Shono, Y., & Stacy, A. W. (2017). Adolescents at risk for drug abuse: A 3-year dual-process analysis. Addiction, 112, 852–863. https://doi.org/10.1111/add.13742.
Article PubMed PubMed Central Google Scholar
Atkins, A. L., Deaux, K. K., & Bieri, J. (1967). Latitude of acceptance and attitude change: Empirical evidence for a reformulation. Journal of Personality and Social Psychology, 6, 47–54. https://doi.org/10.1037/h0024527.
CAS Article PubMed Google Scholar
Barrett, L. F., Tugade, M. M., & Engle, R. W. (2004). Individual differences in working memory capacity and dual-process theories of the mind. Psychological Bulletin, 130, 553–573. https://doi.org/10.1037/0033-2909.130.4.553.
Article PubMed PubMed Central Google Scholar
Barry, A. E., King, J., Sears, C., Harville, C., Bondoc, I., & Joseph, K. (2016). Prioritizing alcohol prevention: Establishing alcohol as the gateway drug and linking age of first drink with illicit drug use. The Journal of School Health, 86, 31–38. https://doi.org/10.1111/josh.12351.
Article PubMed Google Scholar
Bava, S., & Tapert, S. F. (2010). Adolescent brain development and the risk for alcohol and other drug problems. Neuropsychology Review, 20, 398–413. https://doi.org/10.1007/s11065-010-9146-6.
Article PubMed PubMed Central Google Scholar
Beleva, Y., Pike, J. R., Miller, S., Xie, B., Ames, S. L., & Stacy, A. W. (2019). Share of advertising voice at the point-of-sale and its influence on at-risk students’ use of alternative tobacco products. Nicotine & Tobacco Research, 21, 903–910. https://doi.org/10.1093/ntr/nty152.
Article Google Scholar
California Department of Education (2020). Alternative Schools & Programs of Choice. Sacramento, CA. Retrieved from: www.cde.ca.gov.
Chen, C. Y., Storr, C. L., & Anthony, J. C. (2009). Early-onset drug use and risk for drug dependence problems. Addictive Behaviors, 34, 319–322. https://doi.org/10.1016/j.addbeh.2008.10.021.
CAS Article PubMed Google Scholar
de Velasco, J.R., & Gonzales, D. (2017). Accountability for alternative schools in California. Retrieved from Sacramento, CA:
de Velasco, J. R., Austin, G., Dixon, D., Johnson, J., McLaughlin, M., & Perez, L. (2008). Alternative education options: A descriptive study of California continuation high schools. San Diego: California Alternative Education Research Project, San Diego University.
Google Scholar
Deas, D., Riggs, P., Langenbucher, J., Goldman, M., & Brown, S. (2000). Adolescents are not adults: Developmental considerations in alcohol users. Alcoholism: Clinical and Experimental Research, 24, 232–237. https://doi.org/10.1111/j.1530-0277.2000.tb04596.x.
CAS Article Google Scholar
Deaux, K. K., & Bieri, J. (1967). Latitude of acceptance in judgments of masculinity-femininity1. Journal of Personality, 35, 109–117. https://doi.org/10.1111/j.1467-6494.1967.tb01418.x.
CAS Article PubMed Google Scholar
Degenhardt, L., Dierker, L., Chiu, W.T., Medina-Mora, M.E., Neumark, Y., Sampson, N., … Kessler, R.C. (2010). Evaluating the drug use “gateway” theory using cross-national data: Consistency and associations of the order of initiation of drug use among participants in the WHO World Mental Health Surveys. Drug and Alcohol Dependence, 108(1–2), 84–97. doi:https://doi.org/10.1016/j.drugalcdep.2009.12.001.
Dillard, J. P., Shen, L., & Vail, R. G. (2007). Does perceived message effectiveness cause persuasion or vice versa? 17 consistent answers. Human Communication Research, 33, 467–488. https://doi.org/10.1111/j.1468-2958.2007.00308.x.
Article Google Scholar
Eisenberg, M. E., Toumbourou, J. W., Catalano, R. F., & Hemphill, S. A. (2014). Social norms in the development of adolescent substance use: A longitudinal analysis of the International Youth Development Study. Journal of Youth and Adolescence, 43, 1486–1497. https://doi.org/10.1007/s10964-014-0111-1.
Article PubMed PubMed Central Google Scholar
Ennett, S. T., Foshee, V. A., Bauman, K. E., Hussong, A., Faris, R., Hipp, J. R., & Cai, L. (2010). A social contextual analysis of youth cigarette smoking development. Nicotine & Tobacco Research, 12, 950–962. https://doi.org/10.1093/ntr/ntq122.
Article Google Scholar
Fergusson, D. M., Boden, J. M., & Horwood, L. J. (2006). Cannabis use and other illicit drug use: Testing the cannabis gateway hypothesis. Addiction, 101, 556–569. https://doi.org/10.1111/j.1360-0443.2005.01322.x.
Article PubMed Google Scholar
Fishbein, M., & Ajzen, I. (1974). Attitudes towards objects as predictors of single and multiple behavioral criteria. Psychological Review, 81, 59–74. https://doi.org/10.1037/h0035872.
Article Google Scholar
Fritz, M. S., & Mackinnon, D. P. (2007). Required sample size to detect the mediated effect. Psychological Science, 18, 233–239. https://doi.org/10.1111/j.1467-9280.2007.01882.x.
Article PubMed PubMed Central Google Scholar
Garnier, H. E., & Stein, J. A. (2002). An 18-year model of family and peer effects on adolescent drug use and delinquency. Journal of Youth and Adolescence, 31, 45–56. https://doi.org/10.1023/a:1014085016511.
Article Google Scholar
Gilreath, T. D., Chaix, B., King, G., Matthews, S., & Flisher, A. J. (2012). Multi-level influence of school norms on tobacco use in South Africa: An ecometric consideration of group differences. Cancer Causes & Control, 23, 27–36. https://doi.org/10.1007/s10552-012-9908-y.
Article Google Scholar
Graham, J. W., Flay, B. R., Johnson, C. A., Hansen, W. B., Grossman, L., & Sobel, J. L. (1984). Reliability of self-report measures of drug use in prevention research: Evaluation of the Project SMART questionnaire via the test-retest reliability matrix. Journal of Drug Education, 14, 175–193.
CAS Article Google Scholar
Graham, J. W., Olchowski, A. E., & Gilreath, T. D. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory. Prevention Science, 8, 206–213. https://doi.org/10.1007/s11121-007-0070-9.
Article PubMed Google Scholar
Grant, B. F., & Dawson, D. A. (1997). Age at onset of alcohol use and its association with DSM-IV alcohol abuse and dependence: Results from the national longitudinal alcohol epidemiologic survey. Journal of Substance Abuse, 9, 103–110. https://doi.org/10.1016/s0899-3289(97)90009-2.
CAS Article PubMed Google Scholar
Grenard, J. L., Ames, S. L., Wiers, R. W., Thush, C., Sussman, S., & Stacy, A. W. (2008). Working memory capacity moderates the predictive effects of drug-related associations on substance use. Psychology of Addictive Behaviors, 22, 426–432. https://doi.org/10.1037/0893-164X.22.3.426.
Article PubMed PubMed Central Google Scholar
Grunbaum, J. A., Lowry, R., & Kann, L. (2001). Prevalence of health-related behaviors among alternative high school students as compared with students attending regular high schools. Journal of Adolescent Health, 29, 337–343. https://doi.org/10.1016/s1054-139x(01)00304-4.
CAS Article Google Scholar
Hanson, K. L., Thayer, R. E., & Tapert, S. F. (2014). Adolescent marijuana users have elevated risk-taking on the balloon analog risk task. Journal of Psychopharmacology, 28, 1080–1087. https://doi.org/10.1177/0269881114550352.
Article PubMed Google Scholar
Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76, 408–420. https://doi.org/10.1080/03637750903310360.
Article Google Scholar
Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (second ed.). New York: Guilford Publications.
Google Scholar
Hayes, A. F., & Scharkow, M. (2013). The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: Does method really matter? Psychological Science, 24, 1918–1927. https://doi.org/10.1177/0956797613480187.
Article PubMed Google Scholar
Hoffman, B. R., Sussman, S., Unger, J. B., & Valente, T. W. (2006). Peer influences on adolescent cigarette smoking: A theoretical review of the literature. Substance Use & Misuse, 41, 103–155. https://doi.org/10.1080/10826080500368892.
Article Google Scholar
Hovland, C. I., Harvey, O. J., & Sherif, M. (1957). Assimilation and contrast effects in reactions to communication and attitude change. The Journal of Abnormal and Social Psychology, 55, 244–252. https://doi.org/10.1037/h0048480.
CAS Article Google Scholar
Janik, P., Kosticova, M., Pecenak, J. P., & Turcek, M. (2017). Categorization of psychoactive substances into “hard drugs” and “soft drugs”: A critical review of terminology used in current scientific literature. The American Journal of Drug and Alcohol Abuse, 43, 636–646. https://doi.org/10.1080/00952990.2017.1335736.
Article PubMed Google Scholar
Johnson, K., & Taliaferro, L. (2012a). Health behaviors and mental health of students attending alternative high schools: A review of the research literature. Journal for Specialists in Pediatric Nursing, 17, 79–97. https://doi.org/10.1111/j.1744-6155.2011.00311.x.
Article PubMed Google Scholar
Johnson, K. E., & Taliaferro, L. A. (2012b). Health behaviors and mental health of students attending alternative high schools: A review of the research literature. Journal for Specialists in Pediatric Nursing, 17, 79–97.
Article Google Scholar
Johnson, K. E., McMorris, B. J., & Kubik, M. Y. (2013). Comparison of health-risk behaviors among students attending alternative and traditional high schools in Minnesota. The Journal of School Nursing, 29, 343–352. https://doi.org/10.1177/1059840512469409.
Article PubMed Google Scholar
Johnston, L.D., O’Malley, P. M., Miech, R. A., Bachman, J. G., & Schulenberg, J. E. (2016). Monitoring the future national survey results on drug use, 1975–2015: Overview, key findings on adolescent drug use. Ann Arbor: Institute for Social Research, University of Michigan.
Johnston, L.D., Miech, R.A., O’Malley, P.M., Bachman, J.G., Schulenberg, J.E., & Patrick, M.E. (2019). Monitoring the future national survey results on drug use 1975–2018: Overview, key findings on adolescent drug use. Retrieved from Ann Arbor, Michigan.
Kandel, D. (1975). Stages in adolescent involvement in drug use. Science, 190, 912–914. https://doi.org/10.1126/science.1188374.
CAS Article PubMed Google Scholar
Kandel, E. R., & Kandel, D. B. (2014). Shattuck lecture. A molecular basis for nicotine as a gateway drug. The New England Journal of Medicine, 371, 932–943. https://doi.org/10.1056/NEJMsa1405092.
CAS Article PubMed PubMed Central Google Scholar
Kann L, McManus T, Harris WA, et al. Youth Risk Behavior Surveillance — United States, 2015. MMWR Surveill Summ 2016;65(No. SS-6):1–174. https://doi.org/10.15585/mmwr.ss6506a1.
King, K. M., & Chassin, L. (2007). A prospective study of the effects of age of initiation of alcohol and drug use on young adult substance dependence. Journal of Studies on Alcohol and Drugs, 68, 256–265. https://doi.org/10.15288/jsad.2007.68.256.
Article PubMed Google Scholar
Kirby, T., & Barry, A. E. (2012). Alcohol as a gateway drug: A study of US 12th graders. The Journal of School Health, 82, 371–379. https://doi.org/10.1111/j.1746-1561.2012.00712.x.
Article PubMed Google Scholar
LAO. (2015). Next steps for improving state accountability for alternative schools. Retrieved from Sacramento, CA:
Mackinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99. https://doi.org/10.1207/s15327906mbr3901_4.
Article PubMed PubMed Central Google Scholar
McCuller, W. J., Sussman, S., Holiday, K., Craig, S., & Dent, C. W. (2002). Tracking procedures for locating high-risk youth. Evaluation & the Health Professions, 25, 345–362. https://doi.org/10.1177/0163278702025003006.
Article Google Scholar
McCusker, C. G. (2001). Cognitive biases and addiction: An evolution in theory and method. Addiction, 96, 47–56. https://doi.org/10.1080/09652140020016950.
CAS Article PubMed Google Scholar
Miller, J. W., Naimi, T. S., Brewer, R. D., & Jones, S. E. (2007). Binge drinking and associated health risk behaviors among high school students. Pediatrics, 119, 76–85. https://doi.org/10.1542/peds.2006-1517.
Article PubMed Google Scholar
Murray, D. M., Varnell, S. P., & Blitstein, J. L. (2004). Design and analysis of group-randomized trials: A review of recent methodological developments. American Journal of Public Health, 94, 423–432. https://doi.org/10.2105/ajph.94.3.423.
Article PubMed PubMed Central Google Scholar
Newcomb, M. D., & Bentler, P. M. (1988). The impact of family context, deviant attitudes, and emotional distress on adolescent drug use: Longitudinal latent-variable analyses of mothers and their children. Journal of Research in Personality, 22, 154–176. https://doi.org/10.1016/0092-6566(88)90012-8.
Article Google Scholar
Park, H. S., Levine, T. R., Kingsley Westerman, C. Y., Orfgen, T., & Foregger, S. (2007). The effects of argument quality and involvement type on attitude formation and attitude change: A test of dual-process and social judgment predictions. Human Communication Research, 33, 81–102. https://doi.org/10.1111/j.1468-2958.2007.00290.x.
Article Google Scholar
Pike, J. R., Shono, Y., Tan, N., Xie, B., & Stacy, A. W. (2019). Retail outlets prompt associative memories linked to the repeated use of nicotine and tobacco products among alternative high school students in California. Addictive Behaviors, 99, 106067. https://doi.org/10.1016/j.addbeh.2019.106067.
Article PubMed PubMed Central Google Scholar
Reid, L. W., Elifson, K. W., & Sterk, C. E. (2007). Ecstasy and gateway drugs: Initiating the use of ecstasy and other drugs. Annals of Epidemiology, 17, 74–80. https://doi.org/10.1016/j.annepidem.2006.07.013.
Article PubMed Google Scholar
Rohrbach, L. A., Sussman, S., Dent, C. W., & Sun, P. (2005). Tobacco, alcohol, and other drug use among high-risk young people: A five-year longitudinal study from adolescence to emerging adulthood. Journal of Drug Issues, 35, 333–356. https://doi.org/10.1177/002204260503500206.
Article Google Scholar
Rooke, S. E., Hine, D. W., & Thorsteinsson, E. B. (2008). Implicit cognition and substance use: A meta-analysis. Addictive Behaviors, 33, 1314–1328. https://doi.org/10.1016/j.addbeh.2008.06.009.
Article PubMed Google Scholar
Saunders, J., & Saunders, E. (2001). Alternative school students’ perceptions of past [traditional] and current [alternative] school environments. The High School Journal, 85, 12–23.
Article Google Scholar
Schlomer, G. L., Bauman, S., & Card, N. A. (2010). Best practices for missing data management in counseling psychology. Journal of Counseling Psychology, 57, 1–10. https://doi.org/10.1037/a0018082.
Article PubMed Google Scholar
Sherif, M., & Hovland, C.I. (1961). Social judgment: Assimilation and contrast effects in communication and attitude change. Yale University Press. New Haven, CT.
Sherif, C.W., Sherif, M., & Nebergall, R.E. (1965). Attitude and attitude change: The social judgement-involvement process. Saunders Co.: Philadelphia & London.
Smith, S. W., Atkin, C. K., Martell, D., Allen, R., & Hembroff, L. (2006). A social judgment theory approach to conducting formative research in a social norms campaign. Communication Theory, 16, 141–152. https://doi.org/10.1111/j.1468-2885.2006.00009.x.
Article Google Scholar
Spijkerman, R., van den Eijnden, R. J., Vitale, S., & Engels, R. C. (2004). Explaining adolescents’ smoking and drinking behavior: The concept of smoker and drinker prototypes in relation to variables of the theory of planned behavior. Addictive Behaviors, 29, 1615–1622. https://doi.org/10.1016/j.addbeh.2004.02.030.
Article PubMed Google Scholar
Squeglia, L. M., Jacobus, J., & Tapert, S. F. (2009). The influence of substance use on adolescent brain development. Clinical EEG and Neuroscience, 40, 31–38. https://doi.org/10.1177/155005940904000110.
CAS Article PubMed PubMed Central Google Scholar
Stacy, A. W. (1997). Memory activation and expectancy as prospective predictors of alcohol and marijuana use. Journal of Abnormal Psychology, 106, 61–73.
CAS Article Google Scholar
Stacy, A. W., & Wiers, R. W. (2010). Implicit cognition and addiction: A tool for explaining paradoxical behavior. Annual Review of Clinical Psychology, 6, 551–575. https://doi.org/10.1146/annurev.clinpsy.121208.131444.
Article PubMed PubMed Central Google Scholar
Stacy, A. W., Flay, B. R., Sussman, S., Brown, S. K., Santi, S., & Best, J. A. (1990). Validity of alternative self-report indices of smoking among adolescents. Psychological Assessment, 2, 442–446. https://doi.org/10.1037/1040-3590.2.4.442.
Article Google Scholar
Sussman, S., & Ames, S. L. (2001). The social psychology of drug abuse. Buckingham, GB: Open University Press.
Google Scholar
Sussman, S., Stacy, A.W., Dent, C.W., Simon, T.R., Galaif, E.R., Moss, M.A., … Johnson, C.A. (1995). Continuation high schools: Youth at risk for drug abuse. Journal of Drug Education, 25, 191–209. doi:https://doi.org/10.2190/HDQH-XD21-GJT0-9G8V.
Sussman, S., Dent, C. W., & Galaif, E. R. (1997). The correlates of substance abuse and dependence among adolescents at high risk for drug abuse. Journal of Substance Abuse, 9, 241–255. https://doi.org/10.1016/s0899-3289(97)90019-5.
CAS Article PubMed Google Scholar
Sussman, S., Dent, C. W., & Stacy, A. W. (2002). Project towards no drug abuse: A review of the findings and future directions. American Journal of Health Behavior, 26, 354–365. https://doi.org/10.5993/ajhb.26.5.4.
Article PubMed Google Scholar
Sussman, S., Skara, S., & Ames, S. L. (2008). Substance abuse among adolescents. Substance Use & Misuse, 43, 1802–1828. https://doi.org/10.1080/10826080802297302.
Article Google Scholar
Sussman, S., Arpawong, T. E., Sun, P., Tsai, J., Rohrbach, L. A., & Spruijt-Metz, D. (2014). Prevalence and co-occurrence of addictive behaviors among former alternative high school youth. Journal of Behavioral Addictions, 3, 33–40. https://doi.org/10.1556/JBA.3.2014.005.
Article PubMed PubMed Central Google Scholar
Volkow, N. D., Baler, R. D., Compton, W. M., & Weiss, S. R. (2014). Adverse health effects of marijuana use. The New England Journal of Medicine, 370, 2219–2227. https://doi.org/10.1056/NEJMra1402309.
CAS Article PubMed PubMed Central Google Scholar
Wiers, R. W., & Stacy, A. W. (2006). Implicit Cognition and Addiction: An Introduction. In R. W. Wiers & A. W. Stacy (Eds.), Handbook of implicit cognition and addiction (p. 1–8). Sage Publications, Inc. https://doi.org/10.4135/9781412976237.n1.
Zweig, J.M. (2003). Vulnerable youth: Identifying their need for alternative educational settings. The Urban Institute. Washington, D.C.

Download references

Funding

This research was supported by grants from the National Institute on Drug Abuse (DA023368, DA024659, DA024772) and the National Institute on Alcohol Abuse and Alcoholism (AA017996). NIDA and NIAAA had no role in the design of the study, collection, analysis and interpretation of data, or in the writing of the report.

Author information

Authors and Affiliations

Department of Health and Human Sciences, Loyola Marymount University, 1 LMU Drive, MS 8888, Los Angeles, CA, 90045, USA
Christopher Cappelli
School of Community and Global Health, Claremont Graduate University, 675 West Foothill Blvd. Suite 310, Claremont, CA, 91711, USA
Susan L. Ames, Bin Xie, James Russell Pike & Alan W. Stacy

Authors

Christopher Cappelli
View author publications
You can also search for this author in PubMed Google Scholar
Susan L. Ames
View author publications
You can also search for this author in PubMed Google Scholar
Bin Xie
View author publications
You can also search for this author in PubMed Google Scholar
James Russell Pike
View author publications
You can also search for this author in PubMed Google Scholar
Alan W. Stacy
View author publications
You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christopher Cappelli.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Research Involving Human Participants and/or Animals

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Claremont Graduate University Institutional Review Board (IRB00001114) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Cappelli, C., Ames, S.L., Xie, B. et al. Acceptance of Drug Use Mediates Future Hard Drug Use Among At-Risk Adolescent Marijuana, Tobacco, and Alcohol Users. Prev Sci 22, 545–554 (2021). https://doi.org/10.1007/s11121-020-01165-9

Download citation

Accepted: 31 August 2020
Published: 15 September 2020
Issue Date: July 2021
DOI: https://doi.org/10.1007/s11121-020-01165-9

Keywords

Adolescence
Tobacco use
Alcohol use
Marijuana use
Latitude of acceptance
Hard drug use
Alternative high school
Social judgment theory