Income inequality and daily use of cannabis, cigarettes, and e-cigarettes among Canadian secondary school students: Results from COMPASS 2018–19
Bibliographic record
Abstract
INTRODUCTION: Cannabis, cigarette, and e-cigarette use among Canadian adolescents is a major public health concern. Income inequality has been associated with adverse mental health among youth and may contribute to the risk of frequent cannabis, cigarette, and e-cigarette use. We tested the association between income inequality and the risk of daily cannabis, cigarette, and e-cigarette use among Canadian secondary school students. METHODS: We used individual-level survey data from Year 6 (2018/19) of Cannabis, Obesity, Mental health, Physical activity, Alcohol use, Smoking, and Sedentary Behavior (COMPASS) and area-level data from the 2016 Canadian Census. Three-level logistic models were used to assess the relationship between income inequality and adolescent daily and current cannabis use, cigarette smoking, and e-cigarette use. RESULTS: The analytic sample included 74,501 students aged 12-19. Students were most likely to report being male (50.4%), white (69.1%), and having weekly spending money over $100 (23.5%). We found that a standard deviation unit increase in Gini coefficient was significantly associated with increased likelihood of daily cannabis use (OR=1.25, 95% CI = 1.01-1.54) when adjusting for relevant covariates. We found no significant relationship between income inequality and daily smoking. While Gini was not significantly associated with daily e-cigarette use, we observed a significant interaction between Gini and gender (OR=0.87, 95% CI= 0.80-0.94), indicating that increased income inequality was associated with higher risk of reporting daily e-cigarette use among females only. DISCUSSION: An association between income inequality and the likelihood of reporting daily cannabis use across all students and daily e-cigarette use in females were observed. Schools in higher income inequality areas may benefit from targeted prevention and harm reduction programs. Results emphasize the need for upstream discussion on policies that can mitigate the potential effects income inequality.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".