Predictors of Early-Onset Cannabis Use in Adolescence and Risks for Substance Use Disorder Symptoms in Young Adulthood
Bibliographic record
Abstract
Early detection of risks for substance use disorders is essential to lifelong health and well-being for some youth. Very early-onset use is proposed as an indicator of risk for substance use disorders, but risk and protective factors related to early-onset use have not been identified. The current study compared risk and protective factors that distinguish early- and late-onset cannabis users from abstainers using data collected from a large community sample. The study also examined onset-group differences in participants’ reports of substance use disorder symptoms a decade later. Heavy episodic drinking (early-onset: OR = 7.29 CI = [1.60, 33.19]) and engagement with peers involved in deviant behaviors (early-onset: OR = 2.50 CI = [1.50, 4.13]) are risk factors for early-onset cannabis use. Protective factors, including parent monitoring (early-onset: OR = 0.73 CI = [0.58, 0.93]), engagement with peers involved in positive behaviors (early-onset: OR = 0.54 CI = [0.39, 0.76]), school engagement (early-onset: OR = 0.83 CI = [0.72, 0.96]), and academic grades (early-onset: OR = 0.37 CI = [0.21, 0.65]) also predicted early versus later onset-group differences. Early age of onset may be distinctly related to risk and protective factors previously associated with risks for substance use in all adolescents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".