Examining temporal trends in psychological distress and the co-occurrence of common substance use in a population-based sample of grade 7–12 students from 2013 to 2019
Bibliographic record
Abstract
PURPOSE: Characterizing trends and correlates of adolescent psychological distress is important due to observed global increases over the last 20 years. Substance use is a commonly discussed correlate, though we lack an understanding about how co-occurrence of these concerns has been changing over time. METHODS: Data came from repeated, representative, cross-sectional surveys of grade 7-12 students across Ontario, Canada conducted biennially from 2013 to 2019. Poisson regression with robust standard errors was used to examine changes in the joint association between psychological distress (operationalized as Kessler-6 [K6] scores ≥ 13) and substance use over time. Weighted prevalence ratios (PR) and their 99% confidence intervals were estimated, where p < 0.01 denotes statistical significance. RESULTS: The prevalence of psychological distress doubled between 2013 and 2019, with adjusted increases of about 1.2 times each survey year. This biennial increase did not differ based on sex, perceived social standing, school level, or any substance use. Students using substances consistently reported a higher prevalence of psychological distress (between 1.2 times and 2.7 times higher). There were similarly no differential temporal trends based on substance use for very high distress (K6 ≥ 19) or K6 items explored individually. CONCLUSION: Psychological distress steeply increased among adolescents and substance use remains important to assess and address alongside distress. However, the magnitude of temporal increases appears to be similar for adolescents reporting and not reporting substance use.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".