Cannabis, Mental Health and Suicidality: Understanding the Relationship in a University Student Population
Bibliographic record
Abstract
Stressful experiences are common among university students, who also may use cannabis to cope. Previous evidence suggests a relationship between cannabis use and suicidal behaviours; however, the nature of this relationship remains unclear. For example, while acute cannabis use can offer immediate stress relief, high-risk users (e.g., daily or near daily use) may enhance baseline stress, exacerbating mental health symptoms and therefore increasing the risk of presenting suicidal behaviours. This thesis explores the complex relationship between stress, depression, anxiety, and suicidal behaviour among a population of university students, considering the mediating role of cannabis use. Data was collected from Carleton University undergraduates (N = 100) using self-reported questionnaires assessing anxiety (Beck Anxiety Inventory), depression (Beck Depression Inventory), cannabis use (Cannabis Use Disorder Identification Test (Revised); Cannabis Use Questionnaire), stress (Perceived Stress Scale, University Stress Scale), and suicidal behaviours (Suicidal Behavior Questionnaire (Revised)). Findings revealed a significant positive correlation between cannabis use and suicidal behaviour scores. Mediation analysis further indicated that cannabis use mediates the relationship between depression and suicidal ideation. Specifically, higher depression scores were associated with increased cannabis use, which in turn, was linked to greater suicidal behaviour. Further, those who used cannabis to cope, compared to those who used it recreationally, had higher suicide scores. Understanding these pathways is crucial for developing targeted interventions and public health strategies aimed at reducing suicide risk among university students who use cannabis, especially those experiencing stress and mental health challenges.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".