Phenotypes of high-risk drivers based upon driving risk preference
Notice bibliographique
Résumé
High-risk drivers (HRDs) prone to repeat episodes of dangerous driving are over-represented in road traffic morbidity, making them important targets for selective prevention. Better understanding of why some drivers engage in risky behavior is a necessary precursor to the design of effective prevention programs. Nevertheless, HRD research has traditionally focused on healthy young drivers and their self-reported personality features and driving behavior or intentions, and not on bona fide HRD populations and direct observation of their risk-taking behaviors of most concern. In total, the complex nature of HRD is not adequately addressed. A contemporary paradigm for understanding HRD behavior is to identify meaningful subgroups whose members share common characteristics and pathways to risk taking, and thus are likely to show selective treatment responsivity. For this approach, a more integrative, deep-level approach (i.e., beyond reliance on subjective reports) is advantageous. Previous studies by our group into different forms of HRD reveals dysregulation in two neurobiological systems, executive control related to the prefrontal cortex (e.g., 1,2,3), and the cortisol stress response related to the hypothalamic-pituitary-adrenal axis (e.g., 4,5). While promising, the findings are fragmented. We recently recruited three distinct HRD groups (males, N = 138, aged 19-39 years) based upon documented driving behavior: 1) drivers who engage in multiple forms of risk taking; 2) speeders/reckless drivers; and 3) alcohol impaired drivers, as well as a healthy, aged-matched, non-HRD control group. Assessment included psychosocial adjustment, personality, functional tests and assays of the above neurobiological processes, and simulated driving behavior. Our main hypothesis was that each HRD subgroup possesses a distinct biosocial profile. Preliminary results supported our hypothesis. The mixed profile closely resembles a ‘cold’ antisocial phenotype in which chronic under arousal interferes with avoidance learning - which leads to asocial risk seeking. The speeders/reckless drivers comprise a phenotype involving the most dangerous behavior and externalizing features, including competitiveness, impulsivity, reward driven decision-making, and weak inhibitory control. Interestingly, the impaired drivers showed poor inhibitory control, alcohol misuse, yet safe driving in simulation. This suggests that their risk taking involves and interaction between alcohol misuse and poor inhibitory control. Overall, these findings signal that development of novel and targeted HRD interventions may come from more integrative research approaches. References: 1. Bouchard SM, Brown TG, Nadeau L. Decision-making capacities and affective reward anticipation in DWI recidivists compared to non-offenders: A preliminary study. Accident Analysis & Prevention. 2012;45(2):580-7. 2. Ouimet MC, Brown TG, Nadeau L, et al. Neurocognitive characteristics of DUI recidivists. Accident Analysis & Prevention. 2007;39(4):743-50. 3. Dedovic K, Pruessner J, Tremblay J, Nadeau L, Ouimet MC, Lepage M, Brown T.G. Examining cortical thickness in male and female first-time driving while impaired with alcohol offenders. Neuroscience Letters. 2015; In review. 4. Brown TG, Gianoulakis C, Tremblay J, et al. Salivary cortisol: a predictor of convictions for driving under the influence of alcohol? Alcohol & Alcoholism. 2005;40(5):474-81. 5.Ouimet M, Brown TG, Guo F, et al. Higher crash and near-crash rates in teenaged drivers with lower cortisol response: An 18-month longitudinal, naturalistic study. JAMA Pediatrics. 2014;168(6):517-22.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 |
| 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 ».