Cannabis Use During Adolescence and Young Adulthood and Academic Achievement
Bibliographic record
Abstract
Importance: Cannabis use during adolescence and young adulthood may affect academic achievement; however, the magnitude of association remains unclear. Objective: To conduct a systematic review evaluating the association between cannabis use and academic performance. Data Sources: CINAHL, EMBASE, MEDLINE, PsycInfo, PubMed, Scopus, and Web of Science from inception to November 10, 2023. Study Selection: Observational studies examining the association of cannabis use with academic outcomes were selected. The literature search identified 17 622 unique citations. Data Extraction and Synthesis: Pairs of reviewers independently assessed risk of bias and extracted data. Both random-effects models and fixed-effects models were used for meta-analyses, and the Grading of Recommendations Assessment, Development, and Evaluation approach was applied to evaluate the certainty of evidence for each outcome. Data were analyzed from April 6 to May 25, 2024. Main Outcomes and Measures: School grades, school dropout, school absenteeism, grade retention, high school completion, university enrollment, postsecondary degree attainment, and unemployment. Results: Sixty-three studies including 438 329 individuals proved eligible for analysis. Moderate-certainty evidence showed cannabis use during adolescence and young adulthood was probably associated with lower school grades (odds ratio [OR], 0.61 [95% CI, 0.52-0.71] for grade B and above); less likelihood of high school completion (OR, 0.50 [95% CI, 0.33-0.76]), university enrollment (OR, 0.72 [95% CI, 0.60-0.87]), and postsecondary degree attainment (OR, 0.69 [95% CI, 0.62-0.77]); and increased school dropout rate (OR, 2.19 [95% CI, 1.73-2.78]) and school absenteeism (OR, 2.31 [95% CI, 1.76-3.03]). Absolute risk effects ranged from 7% to 14%. Low-certainty evidence suggested that cannabis use may be associated with increased unemployment (OR, 1.50 [95% CI, 1.15-1.96]), with an absolute risk increase of 9%. Subgroup analyses with moderate credibility showed worse academic outcomes for frequent cannabis users and for students who began cannabis use earlier. Conclusions and Relevance: Cannabis use during adolescence and young adulthood was probably associated with increases in school absenteeism and dropout; reduced likelihood of obtaining high academic grades, graduating high school, enrolling in university, and postsecondary degree attainment; and perhaps increased unemployment. Further research is needed to identify interventions and policies that mitigate upstream and downstream factors associated with early cannabis exposure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".