Gender Differences in the Correlates of Adolescents' Cannabis Use
Bibliographic record
Abstract
Adolescents' gender-specific cannabis use rates and their correlates were examined. Data were obtained via a cross-sectional survey conducted in 2004 in British Columbia, Canada, funded by the Canadian Institutes of Health Research. School districts were invited to participate, and schools within consenting districts were recruited. In total, 8,225 students (50% male) from Grades 7 to 12 participated. About 73% were "White," and 47% had used cannabis in their lifetime. Cannabis users were grouped according to their frequency of use: "never users," "frequent users," or "heavy users." Male heavy cannabis users (14.3% of boys) were more likely to be in Grade 9 or higher; be Aboriginal; report poorer economic status; never feel like an outsider; frequently use alcohol and tobacco; and have lower satisfaction with family, friends, and school compared with boys that never used. Female heavy users (8.7% of girls) were more likely to be in a higher grade; report poorer economic status, mental health, and academic performance; frequently use alcohol and tobacco; and have lower satisfaction with their school compared with female never users. Three important gender differences in the multivariate analysis of the correlates of cannabis use were noted: school grade (for boys only), Aboriginal status (for boys only), and mental health (for girls only). Despite the limitations of relying on self-reports, a subset of youth appears to be at risk for excessive cannabis use that may impair life opportunities and health. The gender differences may be important in the design and implementation of prevention or treatment programs for adolescents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".