Reassessing Fitness-to-Drive in Drinker Drivers: The Role of Cognition and Personality
Bibliographic record
Abstract
Drunken driving is among the main challenges for road safety by causing worldwide motor-vehicle crashes with severe injuries and deaths. The reassessment of fitness-to-drive in drivers stopped for drunken driving includes mainly psychological examinations. The present study aimed to investigate the effectiveness and the consistency of selected variables of different psychological driving-related dimensions (i.e., cognitive skills and personality) in discriminating 90 male drinker drivers (DD) from matched non-drinkers controls. The Montreal Cognitive Assessment (MoCA), the Mental Rotation Test (MRT), and the Perspective-Taking Test (PT) were administered to assess overall cognitive functioning, and object- and self-based spatial transformation abilities, respectively. Participants completed a computerized test measuring resilience of attention (DT), reaction times (RT), and perceptual speed (ATAVT). The Personality Psychopathology Five scales (i.e., PSY-5: Aggressiveness-AGGR, Psychoticism-PSYC, Disconstraint-DISC, Negative-Emotionality-NEGE, and Introversion-INTR) the validity scale (L) and the dissimulation index (F-K) were scored from the Minnesota Multiphasic Personality Inventory (MMPI-2). A logistic binomial regression analysis (backward subtraction method) was used to identify discriminant predictors. A prediction analysis (ROC curve method) was performed on the final model. Results showed that the scores obtained in MRT, DT, and the personality measures of PSYC, DISC, NEGE, and INTR significantly discriminated DD from their matched controls with moderate-to-good values of accuracy (0.79), sensitivity (0.80), and specificity (0.79), as well as a good AUC value (0.89). In some cases, the personality dimensions provided-reliable-unexpected results. Low scores of PSYC, NEGE, and INTR were found to predict the membership to the DD group; results are discussed with reference to response management. Personality measures should be assessed with particular attention in a forensic context because they are more prone to be feigned than cognitive ones. Overall, the present study confirmed the relevance of integrating different driving-related psychological dimensions in the evaluation of fitness-to-drive showing the usefulness of standardized tools for the reassessment of drinker drivers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".