Attention deficit hyperactivity disorder, alcohol, drugs and driving: population-based examination in a Canadian sample
Bibliographic record
Abstract
This study examines the relationships among substance use, ADHD, other psychiatric problems, and driving outcomes among a representative sample of adults from Ontario, Canada. The Centre for Addictions and Mental Health Ontario Monitor is an ongoing repeated cross-sectional telephone survey of adults which includes validated measures of: ADHD; psychiatric distress; antisocial behaviour; substance use and problems; driving outcomes. This study presents weighted results (descriptive statistics and regressions) of year one data of a 3-year study. A total of 1999 Ontario residents were sampled, of which 70 (3.5 per cent) screened positively for ADHD. A significantly greater percentage of those who screened positively for ADHD (8.0 per cent) reported at least one crash in the past year compared with those who screened negatively for ADHD. Sequential regression analysis found that age, antisocial personality screen and lifetime cannabis use predicted collisions, while ADHD positive screen, substance abuse positive screen and lifetime cocaine use did not. This first population-based study in Canada showed no relationship between the ADHD screen and collisions when age, sex and kilometres driven are controlled for, while the antisocial personality screen and lifetime cannabis use were significant predictors. However, these analyses are based on self-report screeners, not psychiatric diagnoses and a small sample. Thus, these results should be viewed with caution.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".