Factors associated with the use of psychedelics, ketamine and MDMA among sexual and gender minority youths in Canada: a machine learning analysis
Bibliographic record
Abstract
BACKGROUND: Substance use is increasing among sexual and gender minority youth (SGMY). This increase may be due to changes in social norms and socialisation, or due to SGMY exploring the potential therapeutic value of drugs such as psychedelics. We identified predictors of psychedelics, MDMA and ketamine use. METHODS: Data were obtained from 1414 SGMY participants who completed the ongoing longitudinal 2SLGBTQ+ Tobacco Project in Canada between November 2020 to January 2021. We examined the association between 80 potential features (including sociodemographic factors, mental health-related factors and substance use-related factors) with the use of psychedelics, MDMA and ketamine in the past year. Random forest classifier was used to identify the predictors most associated with reported use of these drugs. RESULTS: 18.1% of participants have used psychedelics in the past year; 21.9% used at least one of the three drugs. Cannabis and cocaine use were the predictors most strongly associated with any of these drugs, while cannabis, but not cocaine use, was the one most associated with psychedelic use. Other mental health and 2SLGBTQ+ stigma-related factors were also associated with the use of these drugs. CONCLUSION: The use of psychedelics, MDMA and ketamine among 2SLGBTQ+ individuals appeared to be largely driven by those who used them together with other drugs. Depression scores also appeared in the top 10 factors associated with these illicit drugs, suggesting that there were individuals who may benefit from the potential therapeutic value of these drugs. These characteristics should be further investigated in future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".