Cannabis cessation among youth: rates, patterns and academic outcomes in a large prospective cohort of Canadian high school students
Bibliographic record
Abstract
INTRODUCTION: Following cannabis legalization in Canada, a better understanding of the prevalence of unprompted cannabis use reduction and subsequent effects on youth academic outcomes is needed to inform harm reduction and health promotion approaches. METHODS: We analyzed a longitudinally linked sample (n = 91774) from the COMPASS prospective cohort study of Canadian high school students attending Grades 9-12 in Ontario and Alberta between 2013-2014 and 2016-2017. We investigated the prevalence of spontaneous cannabis use reduction and cessation between grade transitions (Grades 9-10, 10-11, 11-12) and the effect of cessation on academic achievement (current or recent math and English course marks) and rigour (usual homework completion and past-month truancy). RESULTS: Only 14.8% of cannabis users decreased their use between grades. Of these, two-thirds made only incremental downward changes, a pattern which held true for all three transitions. Cessation rates from daily and weekly use decreased every year. After cessation, students had better odds than continuing users (OR = 1.23, 95% CI: 1.03- 1.48) and worse odds than never-users (OR = 0.55, 95% CI: 0.31-0.97) for some subcategories of math performance. Students who quit cannabis universally improved class attendance (OR = 2.48, 95% CI: 1.93-3.19) and homework completion (OR = 2.32, 95% CI: 1.85-2.92) compared to continuing users. CONCLUSION: Increased academic rigour may underlie any improvements seen in academic performance after cannabis cessation. High school students who use cannabis likely need targeted support to facilitate reduction or cessation and subsequent academic recovery. This indicates that a school-based focus on cannabis harm reduction is justified.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".