Marijuana and Alcohol Use as Predictors of Academic Achievement: A Longitudinal Analysis Among Youth in the COMPASS Study
Bibliographic record
Abstract
BACKGROUND: We tested the effect of initiating marijuana and alcohol use at varying frequencies on academic indices. METHODS: In a sample of 26,475 grade 9-12 students with at least 2 years of linked longitudinal data from year 1 (Y1: 2012-2013), year 2 (Y2: 2013-2014), and year 3 (Y3: 2014-2015) of the COMPASS study, separate multinomial generalized estimating equations models tested the likelihood of responses to measures of academic goals, engagement, preparedness, and performance when shifting from never using alcohol or marijuana at baseline to using them at varying frequencies at follow -up. RESULTS: Students who began using alcohol or marijuana were less likely to attend class regularly, complete their homework, achieve high marks, and value good grades, relative to their abstaining peers. Changing from abstaining to rare/sporadic-to-weekly drinking or rare/sporadic marijuana use predicted aspirations to continue to all levels of higher education, and initiating weekly marijuana use increased the likelihood of college ambitions, while more regular marijuana use reduced the likelihood of wanting to pursue graduate/professional degrees, over high school. CONCLUSIONS: The importance of delaying or preventing substance use is evident in associations with student performance and engagement. The influence on academic goals varied by substance and frequency of initiated use.
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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 | 0.003 |
| 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 | 0.001 | 0.002 |
| 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".