Gender differences in the association between substance use and elevated depressive symptoms in a general adolescent population
Bibliographic record
Abstract
AIMS: This study explores gender differences in the association between substance use and elevated depressive symptoms in the general adolescent population. DESIGN: Cross-sectional self-reported anonymous survey, the 2002/2003 Student Drug Use Survey in the Atlantic Provinces. The sample design was a single-stage cluster sample of randomly selected classes stratified by grade and region. SETTING: The four Atlantic provinces of Canada. PARTICIPANTS: A total of 12 771 students in junior and senior high schools of the public school systems, representing a response rate of about 97%. The average age of participants was 15.2 years. MEASUREMENTS: The measure of elevated depressive symptoms was a 12-item version of the CES-D with three categories of depression risk validated in a companion study. FINDINGS: The prevalence of very elevated depressive symptoms was 8.6% in females and 2.6% in males. Alcohol use and cigarette smoking were found to be independent predictors of elevated depressive symptoms in females, but not males; cannabis use was found to be an independent predictor of elevated depressive symptoms in both males and females. Age was found to have a curvilinear relationship with elevated depressive symptoms in females but not in males. The adolescent's academic performance and province of residence were found to be independent risk factors of elevated depressive symptoms among both males and females. About 10.3% of adolescents considered to be potential candidates for needing help reported having received help because they felt depressed. CONCLUSIONS: The association between depression risk and age, alcohol use, cigarette smoking and cannabis use in the general adolescent population is not straightforward and may differ according to gender. There is unmet need for help for depression among 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".