Adolescent Marijuana Use in the United States and Structural Breaks: An Age-Period-Cohort Analysis, 1991–2018
Bibliographic record
Abstract
To investigate temporal patterns, sociodemographic gradients, and structural breaks in adolescent marijuana use in the United States from 1991 to 2018, we used hierarchical age-period-cohort logistic regression models to distinguish temporal effects of marijuana use among 8th, 10th, and 12th graders from 28 waves of the Monitoring the Future survey (1991-2018). Structural breaks in period effects were further detected via a dynamic-programing-based method. Net of other effects, we found a clear age-related increase in the probability of marijuana use (10.46%, 23.17%, and 31.19% for 8th, 10th, and 12th graders, respectively). Period effects showed a substantial increase over time (from 16.23% in 2006 to 26.38% in 2018), while cohort effects remained stable throughout the study period. Risk of adolescent marijuana use varied by sex, racial group, family status, and parental education. Significant structural breaks during 1995-1996, 2006-2008, and 2011-2013 were identified in different subpopulations. A steady increase in marijuana use among adolescents during the latter years of this time period was identified. Adolescents who were male, were non-Black, lived in nonintact families, and had less educated parents were especially at risk of marijuana usage. Trends in adolescent marijuana use changed significantly during times of economic crisis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".