Do Classes of Polysubstance Use in Adolescence Differentiate Growth in Substances Used in the Transition to Young Adulthood?
Bibliographic record
Abstract
BACKGROUND: Past studies have differentiated classes of polysubstance use in adolescence, however, the associations of adolescent polysubstance use classes with longitudinal substance use trajectories from adolescence to young adulthood have not been studied. OBJECTIVE: The current study examined substance use classes during adolescence and longitudinal trajectories of each substance used across the transition to young adulthood. METHOD: Data were collected biennially from 662 youth and followed 10 years across six measurement assessments. Using baseline data (T1), latent class analysis was used to identify classes of polysubstance use (cigarette, alcohol, marijuana, and illicit drug use) during adolescence. Using T2 through T6 data, we fit latent growth models for cigarette, alcohol, marijuana, and illicit drug use to examine longitudinal trajectories of each substance used by class. RESULTS: A three-class model fit the data best and included a poly-use class, that had high probabilities of use among all substances, a co-use class, that had high probabilities of use among alcohol and marijuana, and a low-use class that had low probabilities of use among all substances. We then examined trajectories of each substance used by class. Strong continuity of substance use was found by class across 14 years. Additionally, for some substances, higher average levels of use of at age 14 were associated with change in growth of other substances used over time. Conclusions/Importance: Efforts that only target a single drug type may be missing an important opportunity to reduce the use and subsequent consequences related to the use of multiple substances.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".