Trajectories of Marijuana Use in Youth Ages 15–25: Implications for Postsecondary Education Experiences
Bibliographic record
Abstract
OBJECTIVE: This study examined associations between longitudinal trajectories of marijuana use from adolescence to young adulthood and postsecondary education (PSE) experiences. Outcomes examined included the type of PSE undertaken, the timing of enrollment, and the likelihood of dropping out. METHOD: Participants (N = 632; 332 females) were from the Victoria Healthy Youth Survey, a five-wave multicohort study of young people interviewed biennially between 2003 and 2011. Latent class growth analysis was used to identify distinct trajectories of the frequency of marijuana use from ages 15 to 25. Logistic regression analyses evaluated class membership as a predictor of the three PSE outcomes, with sex, maternal education, family structure, high school grades, and conduct problems controlled for. RESULTS: Three trajectory groups of marijuana use were identified: abstainers (31%), occasional users (44%), and frequent users (25%). Compared with abstainers, frequent users had the lowest high school grades and the most conduct problems and were least likely to enroll in PSE, especially in a university. Occasional users did not differ from abstainers on high school grades or conduct problems and were no less likely than abstainers to enroll in PSE. However, they delayed enrollment longer and were more likely to drop out of PSE. CONCLUSIONS: Frequent marijuana use from adolescence to young adulthood may close off opportunities for entering PSE, whereas occasional use may create delays in starting and finishing PSE among less at-risk young people. The mechanisms underlying associations between marijuana use and educational difficulties during emerging adulthood as well as adolescence need to be better understood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".