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OBJECTIVE: Previous research has demonstrated different cannabis-related outcomes depending on the goal of cannabis use (i.e., recreational, medical, hybrid of both), underscoring the need to identify variables associated with specific goals of use, particularly in understudied populations. METHOD: This report utilized data from a national survey of menopausal individuals using non-probability sampling. Respondents reporting current regular (≥1x/month) cannabis use (medical n=35, recreational n=61, and hybrid n=102) were included in multivariate logistic regression analyses examining demographic, clinical (e.g., menopause-related symptomatology), and cannabis-related variables associated with goal of cannabis use. RESULTS: Overall, increased number of medical conditions was associated with medical and hybrid use relative to recreational use (ps≤.047), and greater menopause-related symptomatology was associated with medical relative to hybrid use (p=.001). Lower education level was associated with hybrid relative to recreational use (p=.010). Lastly, increased number of modes of use was associated with hybrid use relative to medical and recreational use (ps≤.001). CONCLUSIONS: Results suggest medical and hybrid consumers with more medical conditions and more severe clinical symptoms that are not sufficiently alleviated by conventional treatments may be more open to cannabinoid-based therapies. Additionally, as lower education level is often associated with recreational cannabis use, results suggest hybrid consumers may begin as recreational consumers who then expand their use for medical purposes. Further, more varied modes of use for hybrid consumers may reflect different product selection based on goal of use. Future research should investigate the etiology of hybrid cannabis use and predictors of long-term outcomes associated with goals of use.
RESUMO
Background: Suicide is a leading cause of death, and rates of attempted suicide have increased during the COVID-19 pandemic. The under-diagnosed psychiatric phenotype of dissociation is associated with elevated suicidal self-injury; however, it has largely been left out of attempts to predict and prevent suicide.Objective: We designed an artificial intelligence approach to identify dissociative patients and predict prior suicide attempts in an unbiased, data-driven manner.Method: Participants were 30 controls and 93 treatment-seeking female patients with posttraumatic stress disorder (PTSD) and various levels of dissociation, including some with the PTSD dissociative subtype and some with dissociative identity disorder (DID).Results: Unsupervised learning models identified patients along a spectrum of dissociation. Moreover, supervised learning models accurately predicted prior suicide attempts with an F1 score up to 0.83. DID had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in PTSD and DID.Conclusions: These findings expand our understanding of the dissociative phenotype and underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.
Dissociation, feelings of detachment and disruption in one's sense of self and surroundings, is associated with an elevated risk of suicidal self-injury; however, it has largely been left out of attempts to predict and prevent suicide.Using machine learning techniques, we found dissociative identity disorder had the highest risk of prior suicide attempts, and distinct subtypes of dissociation predicted suicide attempts in posttraumatic stress disorder and dissociative identity disorder.These findings underscore the urgent need to assess for dissociation to identify individuals at high-risk of suicidal self-injury.