Mental Health and Academic Outcomes Associated with Cannabis Use in First Year Undergraduate Students
Bibliographic record
Abstract
Canada is amongst the countries with the highest prevalence of cannabis use is in adolescents and young adults. Early adulthood is a critical period for brain and psychosocial development and coincides with the transition to higher education. Although some negative effects of cannabis on cognition and mental health have been demonstrated in the general population of young people, limited studies have examined this relationship in youth transitioning to higher education. To address this knowledge gap, this thesis describes cannabis use patterns in undergraduate students at entry to university, maps changes in cannabis use over the first academic year and examines the association between cannabis use and symptoms of common mental health problems and academic performance. Data from the biannual Queen’s U-Flourish Student Well-being Survey completed at the start and completion of first year (2021-2023 academic years) was used for the analyses. Frequency of cannabis use was self-reported and the following validated screening measures assessed common mental health challenges: GAD-7 (anxiety), PHQ-9 (depression), SCI-8 (sleep), and SWEMWBS (well-being). At university entry, 20% of students reported at least monthly cannabis use. Of students who reported using cannabis at entry to university, approximately half increased their frequency of use over the academic year. Students with a prior mental illness diagnosis reported more frequent cannabis use (≥3 times per week) than those without a lifetime diagnosis (8.3% vs 2.4%, p<.0001). At school entry, frequent cannabis use was associated with a significantly increased risk of depression (RR: 1.17, 95% CI: 1.06-1.29) and low well-being (RR: 1.57, 95% CI: 1.21-2.04) compared to non-users. Students using cannabis frequently at entry had lower mean cumulative GPA at end of the year (β= -0.26, 95% CI: -0.40 to -0.12). This study underscores the potential risks of frequent cannabis use in first-year undergraduates based on its associations with increased depressive symptoms, lower well-being, and poorer academic performance. Findings support the importance of developing effective health promotion targeting prevention of cannabis use during the transition to university. Future studies investigating amounts, potencies, and motivations for cannabis use could clarify mechanisms driving associated negative effects on mental health and academic outcomes.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".