Trajectories of Single- or Multiple-Substance Use in a Population Representative Sample of Adolescents: Association with Substance-Related and Psychosocial Problems at Age 17
Bibliographic record
Abstract
BACKGROUND: Research is limited regarding the relationship between adolescent substance use and polysubstance use (SU/PSU) as well as their outcomes later in adolescence, while accounting for early risk factors. This study explored substance-related and psychosocial outcomes at age 17 associated with SU/PSU developmental trajectories in a population-representative cohort from Quebec, Canada (N = 1593; 48.4% male), while controlling for preadolescent individual, familial, and social risk factors. SU/PSU included concurrent use of alcohol (AL), cannabis (CA), and other illicit drugs (ODs). METHODS: Self-reported substance use data were collected at ages 12, 13, 15, and 17. Latent growth modeling identified five trajectories: Non-Users (12.8%) and four SU/PSU classes (5.8-37.5%) with varying severity based on onset, frequency, and substance type. Multinomial regression, using non-users as the reference group, assessed trajectory associations with age-17 outcomes. Individual, familial, and social risk factors assessed at ages 10-12 served as control variables. RESULTS: Adolescents in high-risk SU/PSU classes showed the most negative substance-related and psychosocial outcomes compared to non-users and lower-risk SU/PSU classes. Lower-risk SU/PSU classes showed higher maladjustment than non-users. CONCLUSIONS: The findings highlight a dose-response relationship between SU/PSU trajectories and late-adolescent outcomes, independent of preadolescent risk factors. Results emphasize the importance of longitudinal studies that incorporate multiple substances to better capture the complexity of teenagers' involvement in substance use throughout adolescence.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".