Attention Deficit Hyperactivity Disorder, Other Mental Health Problems, Substance Use, and Driving: Examination of a Population-Based, Representative Canadian Sample
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to examine the relationships among self-reported screening measures of attention deficit hyperactivity disorder (ADHD), other psychiatric problems, and driving-related outcomes in a provincially representative sample of adults 18 years and older living in the province of Ontario, Canada. METHODS: The study examined the results of the Centre for Addictions and Mental Health (CAMH) Ontario Monitor, an ongoing repeated cross-sectional telephone survey of Ontario adults over a 2-year period. Measures included ADHD measures (Adult ADHD Self-Report Scale-V1.1 [ASRS-V1.1], previous ADHD diagnosis, ADHD medication use); psychiatric distress measures (General Health Questionnaire [GHQ12], use of pain, anxiety, and depression medication); antisocial behavior measure (The Antisocial Personality Disorder Scale from the Mini-International Neuropsychiatric Interview [APD]); substance use and abuse measures (alcohol, cannabis, and cocaine), Alcohol Use Disorders Identification Test (AUDIT), Alcohol, Smoking and Substance Involvement Screening Test (ASSIST), driving-related outcomes (driving after drinking, driving after cannabis use, street racing, collisions in past year), and sociodemographics (gender, age, vehicle-kilometers traveled). RESULTS: A total of 4,014 Ontario residents were sampled, of which 3,485 reported having a valid driver's license. Overall, 3.22% screened positive for ADHD symptoms on the ASRS-V1.1 screening tool. A greater percentage of those who screened positive were younger, reported previous ADHD diagnosis and medication use, distress, antisocial behavior, anti-anxiety and antidepressant medication use, substance use, and social problems compared to those who screened negative. However, there were no statistically significant differences between those who screened positive or negative for ADHD symptoms on self-reported driving after having 2 or more drinks in the previous hour; within an hour of using cannabis, marijuana, or hash; or in a street race or collision involvement as a driver in the past year. When a sequential regression was conducted to predict self-reported collisions, younger age and higher weekly kilometers driven showed higher odds of collision involvement, and the odds ratio for cannabis use ever approached statistical significance. DISCUSSION: This study is the first population-based study of a representative sample of adults 18 years and older living in Ontario, Canada. These results showed no relationship between the ADHD screen and collision when age, sex, and kilometers driven are controlled for. However, these analyses are based on self-report screeners and not psychiatric diagnoses and a limited sample of ADHD respondents. Thus, these results should be interpreted with caution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".