Autonomy, competence and relatedness and cannabis and alcohol use among youth in Canada: a cross-sectional analysis
Bibliographic record
Abstract
INTRODUCTION: There has been increasing attention on preventing problematic youth substance use in light of concerns about rates of use and policy changes in Canada. Strengths-based approaches that emphasize protective factors, including positive mental health, are at the forefront of current prevention recommendations. However, there is a dearth of research on the association between positive mental health and substance use among youth. This study examines the associations between cannabis and alcohol use among youth and positive mental health as measured through the lens of self-determination theory. METHODS: Secondary analyses of the 2014/2015 Canadian Student Tobacco, Alcohol and Drugs Survey (CSTADS) were conducted. Participating Grade 7 to 12 students residing in Canada completed the Children's Intrinsic Needs Satisfaction Scale (CINSS), which measures autonomy, competence and relatedness, and answered questions that measure past 30-day and more frequent cannabis use, alcohol use and binge-drinking. The associations between autonomy, competence and relatedness and substance use, stratified by sex, were examined using logistic regression. RESULTS: Fully adjusted models revealed that relatedness and competence were associated with lower odds of 30-day and more frequent cannabis use, alcohol use and binge-drinking. Higher autonomy was associated with higher odds of these behaviours. All associations were significant with the exception of competence and more frequent cannabis use among boys, and autonomy and more frequent alcohol use among girls. CONCLUSION: The findings offer new evidence on the associations between positive mental health and substance use among youth, specifically how autonomy, competence and relatedness are associated with cannabis use, alcohol use and binge-drinking. This evidence can be used to inform health promotion and substance use prevention programs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".