1. Predicting Cannabis Use Using Cognitive Measures
Jill M. Robinson, B.A Hons, Marvin Krank, Ph.D.
Cognitions and Substance Use Laboratory
University of British Columbia Okanagan Campus, Kelowna, British Columbia
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Introduction
Cannabis is the most common illicit drug used by adolescents in the world.1
Canadian adolescents report using cannabis more than any other illicit substance with past year usage estimates ranging up to 28% in the adolescent population.2,3
As such, youth is a critical period for drug and alcohol experimentation.4
The decision to initiate cannabis use is mediated by familial, peer, community, and social factors.5
Cognitive measures such as the Affect Misattribution Procedure6 (AMP) and the Outcome Expectancy Liking7 (OEL) tasks have been increasingly used to study attitudes and biases toward substance use.
To date, no study has used the AMP to examine cannabis use in adolescents.
It is important to examine predictors of cannabis use to reduce harm to adolescents.
Methods
565 grade 8 students from a large public school district in Western Canada were tested as a supplemental component of their Health and Career Education class.
As a part of a larger research study, students self-reported previous cannabis, alcohol, and tobacco use patterns, attitudes towards cannabis legalization in Canada, and demographic variables.
Implicit affect misattributions of cannabis were measured using the AMP (see Figure 1).
Explicit cannabis cognitions were assessed using the OEL measure.
Logistic regression analyses assessed implicit affective misattributions and cannabis outcome expectancy liking responses as predictors of cannabis use.
Results
Self-reported patterns of use suggest that 8%, 2.8% and 12% of adolescents in grade 8 have used cannabis, cigarettes, and alcohol respectively in the past year.
Logistic regression analyses revealed that implicit affective rating of cannabis photographs (β = 2.92, 95% CI = [ ], p = 0.001) and outcome expectancy liking ratings (β = 0.96, 95% CI = 2.62 [ ], p = .001) were significantly related to cannabis use in the past year.
Implicit ratings of alcohol pictures were also predictive but to a lesser extent. Interpretation of this result is confounded by the finding that 71% of cannabis users also used alcohol and 47% of alcohol users also used cannabis in the past year. 
Discussion
Results suggest that those who had a one unit increase in total cannabis photograph ratings had a increase in the odds of being a cannabis user in the past year.
Further, those who had a one unit increase in outcome expectancy liking ratings had a 2.62 increase in the odds of being a cannabis user in the past year.
These findings suggest that an implicit measure (AMP) can predict cannabis use in those who have used in the past year above and beyond explicit outcome expectancy measures. This quick task has utility in screening for risky cognitions and behaviours and could be incorporated into drug education curricula.
Many students are using cannabis and alcohol, suggesting the need for more education regarding polysubstance use.
Figure 1: Affect Misattribution Procedure6
Table 2: Pearson Correlational Analyses Ω
Table 1: Logistic Regression Analyses – Variables in the Equation
Exp(B)
Sig.
95% CI for Exp(B) β
Lower
Upper
Affect misattributions (implicit measure)
18.62
.001
5.05
68.63
2.92
Outcome expectancy liking (explicit measure)
2.62
2.01
3.42
.96
% of Cannabis Photos
% of Cannabis People Photos
% of Alcohol People Photos
% of Alcohol Photos
Cannabis Past Year Usage
Alcohol Past Year Usage
Total Cannabis Photos
.84* -
.77*
.83*
.70*
.80*
.25*
.16*
.20*
.13*
.14*
.09*
.17*
.47*
.96*
.73*
.26*
Cannabis OEL
.15*
.19*
.12*
.38*
.24*
.18*
125 millisecond duration
100 millisecond duration
References
Pleasant or Unpleasant Response
1. Rehm, J., & Fischer, B. (2015). Cannabis legalization with strict regulation, the overall superior policy option for public health. Clinical Pharmacology & Therapeutics, 97(6), doi: /cpt.93
2. Statistics Canada. Special Surveys Division. (2015). Canadian tobacco, alcohol and drugs survey. Ottawa, ON: Statistics Canada.
3. UNICEF Office of Research. (2013). Child well-being in rich countries: A comparative overview. UNICEF Office of Research, Venice: Innocenti Report Card 11.
4. Schmits, E., Maurage, P., Thirion, R., & Quertemont, E. (2015). Dissociation between implicit and explicit expectancies of cannabis use in adolescence. Psychiatry Research, 230(3), doi: /j.psychres
5. Porath-Waller, A. (2014). Clearing the smoke on Canadian youths’ perceptions of cannabis. Drug and Alcohol Dependence, 140, e180. doi: /j.drugalcdep
6. Payne, B., Cheng, C., Govorun, O., & Stewart, B. (2005). An inkblot for attitudes: Affect misattribution as implicit measurement. Journal of Personality and Social Psychology, 89(3), doi: /
7. Fulton, H., Krank, M., & Stewart, S. (2012). Outcome expectancy liking: A self-generated, self-coded measure predicts adolescent substance use trajectories. Journal of the Society of Psychologists in Addictive Behaviors, 26(4), doi: /a
* = sig at 0.05
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Poster presented at Issues of Substance Use, Calgary, AB, Nov 13-15, 2017