The Association Between Recreational Cannabis Use and 24‐hour Movement Behaviours: Perceptions of Youth Citizen Scientists
Bibliographic record
Abstract
INTRODUCTION: The prevalence of cannabis use among youth is rising globally, making it crucial to understand its role in health and well-being. While cannabis use is linked to various health outcomes, evidence on its relationship with 24-h movement behaviours (moderate-to-vigorous physical activity, sedentary behaviour, and sleep) in youth is limited. This study uses a digital citizen science approach to examine these associations among Canadian youth aged 13 to 21 years. METHODS: As a part of the Smart Platform, a digital citizen science initiative for ethical population health surveillance and policy interventions, this study engaged with 208 youth citizen scientists from Saskatchewan, Canada (August to December 2018). Participants used their smartphones to report moderate-to-vigorous physical activity, sedentary behaviour, sleep, substance use, mental health, and sociodemographic data over eight consecutive days. Linear regression models assessed associations between cannabis use and 24-h movement behaviours. Sedentary behaviour was further stratified into recreational screen time and other sedentary behaviour to explore distinct relationships with cannabis use. RESULTS: After adjusting for age, gender, parental education, and school, cannabis use was associated with higher hours/day of sedentary behaviour in the overall (β = 8.92, 95% CI = 1.11, 16.72; p-value = 0.02) and weekend models (β = 5.32, 95% CI = 0.89, 9.75; p-value = 0.02). Cannabis use was also associated with higher recreational screen time in both overall (β = 4.65, 95% CI = 0.19, 9.13; p-value = 0.04) and weekend models (β = 2.70, 95% CI = 0.08, 5.32; p-value = 0.04). CONCLUSIONS: These findings need to be confirmed with longitudinal studies to develop holistic population health interventions focusing on policy solutions to address complex negative behaviours among youth.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".