Probabilities of ADD/ADHD and Related Substance Use Among Canadian Adults
Bibliographic record
Abstract
Objective: The aim of this study was to estimate the prevalence and probabilities of comorbidities between self-reported ADD/ADHD and smoking, alcohol binge drinking, and substance use disorders (SUDs) from a national Canadian sample. Method: Data were taken from the Public Use Microdata File of the 2012 Canadian Community Health Survey–Mental Health ( N = 17 311). The prevalence of (a) smoking, (b) alcohol binge drinking, and (c) SUDs was estimated among those with an ADD/ADHD diagnosis versus those without an ADD/ADHD diagnosis. Results: After controlling for potential socioeconomic and mental health covariates, self-reported ADD/ADHD acted as a significant predictor for group membership in the heaviest smoking, heaviest drinking, and heaviest drug usage categories. Conclusion: Individuals self-reporting a diagnosis of ADD/ADHD were found to have a significantly higher likelihood of engaging in smoking and alcohol binge drinking, and were more likely to meet criteria for SUDs than individuals not reporting an ADD/ADHD diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".