Childhood ADHD and addictive behaviours in adolescence: a canadian sample.
Bibliographic record
Abstract
OBJECTIVE: To compare rates of early addictive behaviours in a clinic sample of youth with childhood attention-deficit/hyperactivity disorder (ADHD) with those in community populations. METHOD: We surveyed 142 adolescents (14.1 ± 1.14 years), diagnosed with ADHD before age 12, about early substance use and problem gambling using questions from two cross-sectional population studies: the Canadian National Longitudinal Survey of Children and Youth, Ontario subsample, (N=1,317; 10-15 years) and the Ontario Student Drug Use and Health Survey (N=9,288; 12-18 years). RESULTS: The ADHD sample reported using cigarettes, 17.8% (95% CI 12.1-25.5), alcohol, 27.1% (20.1-35.5), cannabis, 14.2% (8.9-21.7), at a similar or lower rate than the NLSCY (cigarettes, 28.3% (25.8-30.9), alcohol, 28.6% (26.0-31.3), cannabis, 16.5% (14.0-19.4), and OSDUHS samples (cigarettes, 21.9% (20.2-23.7), alcohol, 58.6% (56.0-61.2), cannabis, 26.0% (23.9-28.2). With regards to gambling, there is a non-significant trend for ADHD youth to report gambling more frequently than the provincial average, 7.9% (3.3-17.9) vs. 4.3% (2.9-6.3). CONCLUSIONS: Our findings support the emerging literature that youth diagnosed with ADHD in childhood may not be at greater risk for onset of substance use in early adolescence. The study identified two areas that warrant further investigation in this population; the possible increased risk for substance use among females and a trend toward early onset of gambling behaviours.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".