Smoking, alcohol and drug use in youth and adults with attention-deficit hyperactivity disorder
Bibliographic record
Abstract
BACKGROUND: Previous research suggests a relationship between attention-deficit hyperactivity disorder (ADHD) and smoking, alcohol and illicit drug use, however most studies have focused on adolescents or young adults, or clinically ascertained samples. AIMS: To analyse population-based data on the relationship between ADHD and at-risk health behaviours in adolescents and adults. METHOD: Data were derived from a Statistics Canada population-based health survey. The association between the diagnosis of ADHD and smoking, alcohol use, and illicit drug use was examined. RESULTS: Individuals with ADHD started smoking at a younger age. They consumed more alcoholic drinks on drinking days, and women with ADHD were more likely to engage in binge drinking. Women over the age of 25 and men with ADHD were more likely to meet alcohol-dependence lifetime criteria. People with ADHD were at a greater risk of drug misuse and dependence. CONCLUSIONS: People with ADHD are more likely to partake in at-risk behaviours. DECLARATION OF INTEREST: None. COPYRIGHT AND USAGE: © The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".