Identifying early intervention opportunities for illicit stimulant use: A cross-sectional study of factors associated with illicit stimulant use among young people accessing integrated youth services in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: Illicit stimulant (cocaine and/or amphetamine) use among young people aged 12-24 is a public health priority given that substance use initiation tends to peak in this developmental period and significant associated immediate and long-term harms are associated with its use. Young people using stimulants must be engaged in services as early as possible to reduce these harms. To inform early intervention opportunities, this study aimed to identify the risk/protective factors associated with illicit stimulant use among young people. METHODS: We conducted a cross-sectional study on routinely collected self-reported data among young people accessing integrated youth services in British Columbia (Canada) between April 2018 and January 2022. Data were collected on young peoples' socio-demographic characteristics, and social, behavioral, and health profiles. Variable selection was guided by established risk/protective factors for substance use among young people. The study used multivariable logistic regression to identify risk/protective factors that were independently associated with past 30-day illicit stimulant use. RESULTS: The analytic sample included n = 5620 young people aged 12-24 and a total of 163 (2.9 %) reported past 30-day illicit cocaine and/or amphetamine use. Demographic characteristics that were independently associated with illicit stimulant use included older age (aOR = 1.27, 95 % CI = 1.17-1.38) and gender identity as man vs woman (aOR = 1.71, 95 % CI = 1.10-2.70). Social and environmental risk factors included recently witnessing or experiencing violence (aOR = 2.32, 95 % CI = 1.47-3.68) and higher past-year crime/violent behaviors score (aOR = 1.39, 95 % CI = 1.13-1.69). Finally, regular alcohol (aOR = 6.90, 95 % CI = 2.36-25.42), regular (aOR = 3.74, 95 % CI = 1.95-7.54) or social (aOR = 3.06, 95 % CI = 1.44-6.60) tobacco use, and lifetime hallucinogen (aOR = 3.24, 95 % CI = 1.8-5.91) and ecstasy/MDMA (aOR = 2.53, 95 % CI = 1.48-4.39) use were also statistically significant risk factors. CONCLUSIONS: These risk/protective factors support identification of young people who may benefit from further screening, assessment, and treatment for illicit stimulant use. This study also underscores the need to expand early intervention and harm reduction programs that can comprehensively respond to young peoples' stimulant use, health, and social needs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".