Exploring the dynamic transitions of polysubstance use patterns among Canadian youth using Latent Markov Models on COMPASS data
Bibliographic record
Abstract
Background: There are limited evidence on polysubstance use (PSU) patterns among youth, with little understanding of associated factors. We addressed this deficiency by examining PSU patterns and impact factors in a large sample of Canadian youth. Methods: Data were from linked samples of 8824 Canadian secondary school students who completed longitudinal health surveys of the COMPASS study for three-consecutive-year (Y2016/17, Y2017/18, Y2018/19). Substance use measures, including cigarettes, e-cigarettes, alcohol, and marijuana, were self-reported and categorized into never/occasional/current use. We developed a multivariate Latent Markov Model to investigate PSU patterns. Model selection was based on the Bayesian information criterion and the goodness-of-fit test. Findings: Four distinct use patterns were identified: no use (C1), occasional single-use of alcohol (C2), dual-use of e-cigarettes and alcohol (C3), and current multi-use (C4). The probabilities of initial class membership were PC1=0·589, PC2=0·216, PC3=0·149, and PC4=0·047 (Y2016/17). C1 had the highest prevalence (60·3%) in Y2016/17, and C3 became the prominent use pattern (32·4%) by Y2018/19. The marginal distribution of C3 (0·149->0·265->0·341) and C4 (0·047->0·110->0·202) steadily increased over time, indicating a growing trend of using dual and multiple substances. Risk factors associated with PSU patterns included truancy (ORC2=1·67, 95% CI 1·55-1·79; ORC3=1·92, 1·80-2·04; ORC4=2·79, 2·64-2·94), more smoking friends, more weekly allowance, elevated BMI, being older, and attending unsupportive schools. In contrast, not gambling online (ORC2=0·22, -0·16-0·58; ORC3=0·14, -0·24-0·52; ORC4=0·08, -0·47-0·63), eating breakfast, residing in urban areas, and having higher school connectedness had positive effects on initial membership of PSU patterns. Interpretation: The diverse associations between PSU patterns and multifaceted factors should be considered when designing and implementing interventions targeting the risk reduction of youth PSU. Funding: The University of Waterloo scholarship and Microsoft AI for Good grant; the Canadian Institutes of Health Research, Health Canada, the Canadian Centre on Substance Abuse, the SickKids Foundation, the Ministère de la Santé et des Services sociaux of the province of Québec. Declaration of Interest: We declare no competing interests. Ethical Approval: The COMPASS study has received ethics clearance from the University of Waterloo Office of Research Ethics (ORE 30118).
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".