scholarly journals Gender differences in cannabis use disorder treatment: Change readiness and taking steps predict worse cannabis outcomes for women

2016 ◽  
Vol 60 ◽  
pp. 197-202 ◽  
Author(s):  
Brian J. Sherman ◽  
Nathaniel L. Baker ◽  
Aimee L. McRae-Clark
Keyword(s):  
Gender Differences ◽  
Change Readiness ◽  
Cannabis Use ◽  
Cannabis Use Disorder ◽  
Treatment Change ◽  
Disorder Treatment

scholarly journals Thinking Beyond Legalization: The Case for Expanding Evidence-Based Options for Cannabis Use Disorder Treatment in Canada

2018 ◽  
Vol 64 (2) ◽  
pp. 82-87 ◽  
Author(s):  
Didier Jutras-Aswad ◽  
Bernard Le Foll ◽  
Julie Bruneau ◽  
T. Cameron Wild ◽  
Evan Wood ◽  
...  
Keyword(s):  
Cannabis Use ◽  
Evidence Based ◽  
Cannabis Use Disorder ◽  
Disorder Treatment

Who responds best to text-delivered cannabis use disorder treatment? A randomized clinical trial with young adults.

2018 ◽  
Vol 32 (7) ◽  
pp. 699-709 ◽  
Author(s):  
Michael J. Mason ◽  
Nikola M. Zaharakis ◽  
Matthew Moore ◽  
Aaron Brown ◽  
Claudia Garcia ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Young Adults ◽  
Randomized Clinical Trial ◽  
Cannabis Use ◽  
Cannabis Use Disorder ◽  
Disorder Treatment

Cannabis use disorder treatment barriers and facilitators among veterans with PTSD.

2016 ◽  
Vol 30 (1) ◽  
pp. 73-81 ◽  
Author(s):  
Sarah J. Bujarski ◽  
Jessica N. Galang ◽  
Nicole A. Short ◽  
Jodie A. Trafton ◽  
Elizabeth V. Gifford ◽  
...  
Keyword(s):  
Cannabis Use ◽  
Barriers And Facilitators ◽  
Treatment Barriers ◽  
Cannabis Use Disorder ◽  
Disorder Treatment

scholarly journals Explainable Machine Learning Analysis Reveals Gender Differences in the Phenotypic and Neurobiological Markers of Cannabis Use Disorder

2021 ◽  
Author(s):  
Gregory Niklason ◽  
Eric Rawls ◽  
Sisi Ma ◽  
Erich Kummerfeld ◽  
Andrea M. Maxwell ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Risk Factors ◽  
Gender Differences ◽  
Community Sample ◽  
Cannabis Use ◽  
Data Driven ◽  
Cannabis Use Disorder ◽  
Cannabis Dependence ◽  
Human Connectome Project

Background: Cannabis Use Disorder (CUD) has been linked to environmental, personality, mental health, neurocognitive and neurobiological risk factors. While many studies have revealed gender differences in CUD, the relative importance of these complex factors by gender has not been described. Methods: We conducted a data-driven examination of gender differences in CUD in a community sample of young adults (Human Connectome Project [HCP]; n = 1204, 54% female). We employed state-of-the-art machine learning methods [gradient tree boosting, XGBoost] in combination with novel factor ranking tools [SHapley's Additive exPlanations (SHAP)] as an 'explainable machine learning approach' in the multimodal data collected by the HCP (phenotypic and brain data). Results: We were able to successfully classify both cannabis dependence and cannabis use levels. Previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification. Predominantly-male risk factors included personality (high openness), mental health (high externalizing, high childhood conduct disorder, high fear somaticism), neurocognitive (impulsive delay discounting, slow working memory performance) and brain (low hippocampal volume) factors. Conversely, predominantly-female risk factors included environmental (low education level, low instrumental support) factors. Conclusions: Our data-driven analysis of gender differences in the multimodal risk factors underlying cannabis dependence and use levels demonstrate that environmental factors contribute more strongly to CUD in women, whereas individual factors such as personality, mental health and neurocognitive factors have a larger importance in men. This warrants further investigations, and suggests the importance of understanding how these differences relate to the development of effective treatment approaches.

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