Associations between Longitudinal Patterns of Substance Use and Anxiety and Depression Symptoms among a Sample of Canadian Secondary School Students
Bibliographic record
Abstract
The objective of this study is to examine the longitudinal associations between latent classes of substance use and anxiety and depression scores among youth who use substances. This study uses data from three waves (Wave 1: 2017/18, Wave 2: 2018/19, and Wave 3: 2019/20) of the COMPASS study. Students in grades 9 and 10 who reported substance use at baseline (n = 738) report their substance use (alcohol, cannabis, cigarettes, and e-cigarettes) and anxiety and depression symptoms at each wave. A Repeated Measures Latent Class Analysis (RMLCA) is used to determine substance use classes, and mixed models are used to examine the associations between substance use classes and anxiety and depression. We identify three classes of substance use: (1) occasional alcohol and e-cigarette use, (2) escalating poly-substance use, and (3) consistent poly-substance use. After controlling for relevant covariates, consistent poly-substance use is associated with depression (Female OR: 1.24 [95%CI: 0.46, 2.02]; Male OR 1.13 [95%CI: 0.38, 1.87]) but not anxiety. Escalating poly-substance use is associated with depression among males (OR 0.72 [95%CI: 0.10, 1.33]). These findings should be taken into consideration when creating prevention programming and treatment strategies for adolescents. Substance use programming should be comprehensive, consider multiple substances, and be cognizant of symptoms of mental illness, particularly depression.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".