From the brain to bad behaviour and back again: Neurocognitive and psychobiological mechanisms of driving while impaired by alcohol
Bibliographic record
Abstract
ISSUES: Driving while impaired by alcohol (DWI) is responsible for substantial mortality and injury. Significant gaps in our understanding of DWI re-offending, or recidivism, reduce our ability to practically assess recidivism probability and to match interventions to individual risk profiles. These shortcomings reflect the baffling heterogeneity in the DWI population and the limited focus of much existing DWI recidivism research to psychosocial, psychological and substance use correlates. APPROACH: This narrative review summarises the evidence for the contribution of neurocognitive and psychobiological mechanisms to DWI behaviour and recidivism. Given the nascent nature of this literature, insight into the putative contribution of these mechanisms to DWI is also drawn from other experimental literatures, particularly those on alcohol use disorders and cognitive and behavioural neuroscience. KEY FINDINGS: Alcohol-related neurotoxicity and dysregulation of hypothalamic-pituitary-adrenal axis and serotonergic systems may underlie certain offender characteristics consistently correlated with heightened DWI risk, persistence and intervention resistance. Their markers are less vulnerable to sources of bias than subjective psychosocial indices and are more far-reaching than alcohol abuse in explaining DWI behaviour and recidivism. Implications. The investigation of neurocognitive and psychobiological mechanisms in DWI research is a promising avenue for discerning clinically meaningful subgroups within the DWI population. This can lead to research and development in alternative assessment and more targeted intervention technologies. CONCLUSION: Multidimensional research in DWI and recidivism offers novel avenues for increasing road safety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".