The association between depression, anxiety and substance use among Canadian post-secondary students
Bibliographic record
Abstract
Purpose: This study aims to examine the association between depression, anxiety and substance use among Canadian post-secondary students. Methods: This study used data from the spring 2016, American College Health Association – National College Health Assessment II (ACHA-NCHA II) survey. It includes 43,780 college students from 41 Canadian post-secondary institutions. The exposure variables of interest were alcohol, cannabis and tobacco use, and the outcome variables of interest were diagnosis or treatment for depression and/or anxiety. Descriptive statistics, univariate and multivariate logistic regression models were used to analyze our data. Results: Our study found that 14.7% of post-secondary students were diagnosed or treated for depression and 18.4% for anxiety within the past 12 months. Among current (past 30 days) substance use, it was reported that alcohol (69.3%), cannabis (17.9%) and tobacco (11%) were the most common. There was a significant association between depression and current tobacco use (OR =1.36, 95% CI: 1.22–1.52, P <0.001) and current cannabis use (OR =1.17, 95% CI: 1.05–1.31, P <0.001). There was also a gender-specific association between anxiety and female alcohol users (OR =1.41, 95% CI: 1.24–1.62, P <0.001). Conclusion: The results of this study found significant associations between depression, tobacco use and cannabis use, and anxiety and alcohol use among post-secondary students. These conditions should be screened concurrently for improved outcomes among this vulnerable population. Keywords: depression, anxiety, alcohol, cannabis, tobacco, post-secondary students
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".