Getting high to cope with COVID-19: Modelling the associations between cannabis demand, coping motives, and cannabis use and problems

Objective: Research conducted with young adults has consistently identified cannabis use motives as a predictor for cannabis use and problems, but comparatively few studies have examined cannabis motives in adult samples. Furthermore, although substantive evidence has identified links between cannabis use and psychiatric conditions, limited research has examined the intersection of cannabis use, motives, and psychopathology. The present study sought to characterize cannabis use motives in an adult sample and investigate whether those motives would link relationships between cannabis misuse and psychiatric symptoms. Method: Participants (N=395; M age=34.8; %F=47.6; % White = 81.3%) completed assessments related to cannabis misuse, cannabis use motives, and symptoms of depression, anxiety, PTSD, and somatic experiences. Bivariate correlations, hierarchical regressions, and subsequently, mediation analyses were performed to examine associations between motives and cannabis misuse, and to investigate whether particular motives would link the relationship between cannabis misuse and psychiatric symptoms. Results: Analyses revealed statistically significant, unique associations between cannabis misuse and Social (β = .13), Enhancement (β = .12) and, particularly, Coping motives (β = .48). Further, Coping fully linked the relationship between level of cannabis misuse and depressive, anxiety, PTSD, and somatic symptoms. Conclusion: These results lend support for a negative reinforcement motivational profile as the predominant pattern in adult cannabis users, especially with regard to concurrent symptoms of cannabis use disorder and psychopathology. These results suggest that targeting maladaptive coping with cannabis may prove an effective treatment target in addressing co-occurring cannabis misuse and psychopathology.

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The impact of depressed mood and coping motives on cannabis use quantity across the menstrual cycle in those with and without pre‐menstrual dysphoric disorder

Addiction ◽

10.1111/add.15465 ◽

2021 ◽

Author(s):

Kayla M. Joyce ◽

Kara Thompson ◽

Kimberley P. Good ◽

Philip G. Tibbo ◽

M. Elizabeth O'Leary ◽

...

Keyword(s):

Menstrual Cycle ◽

Depressed Mood ◽

Cannabis Use ◽

Coping Motives ◽

And Coping ◽

The Impact

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Self-Reported Symptoms of Cannabis Use Disorder (SRSCUD): Psychometric Testing and Validation

10.26828/cannabis.2021.01.000.16 ◽

2021 ◽

Author(s):

Melissa Sotelo ◽

Dylan Richards ◽

Matthew Pearson ◽

Protective Strategies Study Team

Keyword(s):

Factor Analysis ◽

Convergent Validity ◽

Psychometric Testing ◽

Preliminary Evidence ◽

Cannabis Use ◽

Continuous Measure ◽

Cannabis Use Disorder ◽

Correlated Errors ◽

Behavioral Strategies ◽

Coping Motives

Findings from national surveys demonstrate that cannabis use peaks in young adulthood and that the annual prevalence of marijuana use among young adults (34.0%) is the highest it has been in decades (Johnston et al., 2016). We developed a 13 item measure designed to characterize the 11 symptoms of CUD as described in the DSM 5 (APA, 2013). To evaluate the performance of this Self Reported Symptoms of Cannabis Use Disorder (SRSCUD) measure, we examined its associations with other measures of CUD symptoms, negative cannabis related consequences, and other known risk factors for CUD (i.e., coping motives). Colleges students (n =7000) recruited from 9 universities in 9 states throughout the U.S. Our analyses focus on past month cannabis users (n = 2077). We split our sample in half to conduct exploratory factor analysis (EFA,n = 1011) and confirmatory factor analysis (CFA, n = 1012). All items loaded saliently on a single factor of CUD symptoms in both EFA (.553 = λ = 805) and CFA models (.524 = λ = 830) (see Table 1). In our final model, we allowed correlated errors between the two indicators of tolerance (items 10 and 11) and the two indicators of withdrawal (items 12 and 13), and obtained acceptable model fit across most indices: CFI = .941, TLI = .927, RMSEA = .059, SRMR = .042. As shown in Table 2, the total score of the SRSCUD was strongly correlated with other CUD symptoms measures (.617 < r s < .697), demonstrating convergent validity. SRSCUD was moderately positively correlated with a well known risk factor for CUD (coping motives) and moderately negative correlated with a well known protective (cannabis protective behavioral strategies). We conducted receiver operator characteristic (ROC) curve analyses to identify well how our continuous measure of CUD symptoms could identify individuals who exceed the cutoffs for probable CUD on these other symptom measures. For the most well validated measure (CUDIT R), we had excellent sensitivity/specificity (mean score of 1.5 on SRSCUD) for predicting probable CUD. Although more research evaluating performance of the SRSCUD compared to a clinical diagnosis is needed, we have preliminary evidence for construct validity of this measure.

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