Factors associated with cannabis use in early adolescence
Bibliographic record
Abstract
INTRODUCTION: We examined whether factors identified as associated with cannabis use at age 14 to 16 years are also associated with ever use at age 12. METHODS: Participants in the AdoQuest study (n = 1852) were recruited in 2005 from among Grade 5 students in 29 French-language elementary schools in Montréal, Canada. Self-report data were collected from participants in Grade 5 (spring 2005) and 6 (fall 2005 and spring 2006) and from parents/guardians in 2006/07. Inclusion in the analytic sample (n = 1076; mean age [SD] = 10.7 [0.5]) required data from participant and parental questionnaires and data on cannabis use in Grade 6 (mean age [SD] = 11.7 [0.4]). We estimated associations between ever use at age 12 with 33 potential correlates, separately in unadjusted and adjusted logistic regression models. RESULTS: Fifty-three participants (4.9%) reported ever use at age 12. Factors associated with higher odds of ever use included older age, identifying as male, lower household income, more weekly spending money, ever tried cigarettes or other tobacco products, ever drank alcohol or binge drank, ever gambled, friends or siblings smoke cigarettes, greater nicotine dependence, higher depressive symptoms and greater impulsivity. Protective factors included higher levels of parental/guardian monitoring and greater self-esteem and school connectedness. CONCLUSION: Factors associated with cannabis use at later ages are also associated with ever use at age 12. Our findings suggest that surveillance for and interventions to prevent cannabis use are warranted before age 12.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".