Attention Deficit Hyperactivity Disorder, Other Mental Health Problems, Substance Use, and Driving: Examination of a Population-Based, Representative Canadian Sample
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».