Untangling the Relationship Between Income Inequality and Poly-Substance Use Among Adolescents in Canada
Bibliographic record
Abstract
Growing evidence suggests that income inequality is associated with adolescent substance use behaviors, prompting the presumption that it may also be associated with poly-substance use. This study examined the association between income inequality and poly-substance use and tested whether anxiety indirectly affects this relationship. We used cross-sectional survey data from the 2018–2019 Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, and Sedentary behavior (COMPASS) project. The analytical sample comprised 71,396 students from 136 secondary schools and 43 census divisions across four Canadian provinces. Using multilevel path analyses, we examined the direct and indirect association between income inequality and poly-substance use. Students in neighborhoods with moderate income inequality relative to those in low-income inequality neighborhoods had higher odds of poly-substance use, with similar odds observed for both females (OR mod = 1.34; 95% confidence interval [CI] = [1.02, 1.66]) and males (OR mod = 1.38; 95% CI = [1.05, 1.72]). In relation to the indirect association through anxiety, gender-stratified results showed that attending schools in neighborhoods with moderate- and high-income inequality was associated with slightly higher odds of poly-substance use among females (OR mod = 1.09; 95% CI = [1.01, 1.18] and OR high = 1.10; 95% CI = [1.00, 1.21]). Income inequality is associated with poly-substance use, and this relationship is also indirectly influenced by anxiety among females. Reducing neighborhood income inequality may contribute to lowering poly-substance use among adolescents. In addition, anxiety management in schools located in highly unequal neighborhoods may be an effective intervention approach in reducing poly-substance use among female adolescents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".