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Comparing Cannabis Use Motivations and Dependence Across Regular Cannabis Users Who Have or Have Not Recently Used Psilocybin.

Madeline K StangerHarmony O SofferAngela D BryanCarillon J Skrzynski
Published in: Cannabis and cannabinoid research (2024)
Introduction: In Colorado, both cannabis and psilocybin are legal and becoming more commonly used. However, there is almost no research detailing the public health concerns regarding negative outcomes (e.g., dependence) of cannabis and psilocybin co-use and motives that may perpetuate these negative outcomes (e.g., coping, boredom). Methods: Using data from a larger observational study on cannabis and metabolic processes, regular cannabis users (use ≥7 times/month; n = 97, 35.1% female, 89.7% WHITE) who used psilocybin in the past 3 months ( n = 34) were compared with those who had not used psilocybin in the past 3 months ( n = 63) on cannabis dependence as measured by the Marijuana Dependence Scale and endorsement of 12 cannabis motives from the Comprehensive Marijuana Motives Questionnaire. Correlations between motives and dependence were also examined and compared across groups. Results: Findings revealed that individuals who had recently used psilocybin had greater cannabis dependence scores than those who had not used recently [ F (1, 95) = 5.53, p = 0.02], and more strongly endorsed that their cannabis use was motivated by enjoyment [ F (1, 91) = 4.31, p = 0.04], boredom [ F (1, 91) = 9.10, p < 0.01], and availability [ F (1, 91) = 9.46, p < 0.01]. Correlations between dependence scores and coping and boredom motives were also significantly positive for both groups (all p values <0.05) whereas positive correlations with experimentation, celebration, and availability motives were only significant for recent psilocybin users (all p values <0.05). Discussion: These results suggest there are motivational differences for cannabis use among those who co-use cannabis and psilocybin, and there may be a greater risk for harm for these individuals.
Keyphrases
  • public health
  • type diabetes
  • depressive symptoms
  • metabolic syndrome
  • big data
  • deep learning
  • patient reported