Bibliographic record
Abstract
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.
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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 | 0.000 |
| 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".