Cannabis use and mental health among young sexual and gender minority men : a qualitative study
Bibliographic record
Abstract
Despite a growing body of evidence demonstrating that cannabis use is associated with mental illness among sexual and gender minority (SGM) men, little is known about the motivations, patterns and contexts that influence this relationship. Our study aimed to characterize how cannabis use features within the mental health-related experiences of young SGM men in Vancouver, Canada. From January to December 2018, semi-structured interviews were conducted with 50 SGM men ages 15 to 30 years to explore their experiences using cannabis. We draw on thematic analysis to reveal three themes regarding participants’ experiences with cannabis use and mental health. First, participants experiences emphasized the interconnectedness of cannabis use, sexual, and mental health, including using cannabis to: (i) cope with mental health symptoms during sexual encounters (e.g., anxiety, sexual trauma-related stress); and (ii) substitute or replace other substances (e.g., crystal methamphetamine, MDMA) to reduce drug-related harms in Chemsex practices (e.g., decreased ability to consent, drug-induced psychosis). Second, participants discussed the instrumental use of cannabis to alleviate and address symptoms of mental health (e.g., depression, post-traumatic experiences). Third, participants described adverse effects of cannabis use on their mental health, including feelings of paranoia that they associated with cannabis use, as well as concerns around developing cannabis dependence. Our findings reveal important implications for public health policy on how cannabis can be used to manage experiences of mental health among young SGM men, while also highlighting the need to develop harm reduction services for those who may experience mental health-related harms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".