Age of Cannabis Use Onset and Adult Drug Abuse Symptoms: A Prospective Study of Common Risk Factors and Indirect Effects
Bibliographic record
Abstract
The present study examined 1) whether the associations between cannabis use (CU) age of onset and drug abuse by 28 y remain when controlling for risk factors in childhood, adolescence and early adulthood; and 2) the developmental pathways from early risk factors to drug abuse problems. Participants from a longitudinal sample of boys of low socioeconomic status ( N = 1,030) were followed from 6 to 28 y. We examined the self-reported CU onset between the ages of 13 and 17 y and drug abuse symptoms by 28 y. The odds of developing any drug abuse symptoms by 28 y were reduced by 31% for each year of delayed CU onset (OR = 0.69). Cannabis, alcohol and other drug frequency at 17 y mediated this association. Still, even when taking that frequency of use into account, adolescents who started using cannabis before 15 y were at a higher risk of developing drug abuse symptoms by age 28 y. Significant indirect effects were found from early adolescent delinquency and affiliation with deviant friends to drug abuse symptoms at 28 y through CU age of onset and substance use frequency at 17 y. The results suggest more clearly than before that prevention programs should aim at delaying CU onset to prevent or reduce drug abuse in adulthood. Furthermore, prevention programs targeting delinquency and/or affiliation with deviant friends in childhood or early adolescence could indirectly reduce substance abuse in adulthood without addressing substance use specifically.
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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 | 0.001 |
| 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 | 0.001 | 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".