The association of cannabis use with fast-food consumption, overweight, and obesity among adolescents aged 12-15 years from 28 countries
Bibliographic record
Abstract
Background Cannabis legalization and use have risen globally. However, the association between cannabis use, eating behaviors and body weight among adolescents is yet unexplored.Objectives This study examined the association between cannabis use, fast-food consumption, overweight and obesity in 28 countries using data from the 2010–2016 Global School-Based Student Health Survey.Methods Multivariable logistic regression and meta-analysis were performed among a sample of 83,726 adolescents (48.7% females) aged 12–15 years, mean (SD) age of 13.8 (0.9) years.Results The overall prevalence of cannabis use (in past 30 days) and fast-food consumption (in past 7 days) were 2.8% and 57.3% respectively. The overall prevalence of overweight and obesity was 14.7% and 4.2%, respectively. Cannabis use was significantly associated with fast-food consumption (OR = 1.33; 95%CI = 1.13–1.57) but not with overweight (OR = 0.95; 95%CI = 0.80–1.14) or obesity (OR = 1.16; 95%CI = 0.85–1.59). For obesity, there was a moderate level of between-country heterogeneity (I2 = 51.9%) and significant positive associations with cannabis use were observed in Bahamas, Bangladesh, Namibia and Nepal. Conclusion The results highlight the association between cannabis use and dietary risks, providing evidence for public health interventions on the interrelated nature of cannabis use and fast-food consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| 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".