The influence of depression symptoms and antidepressant medications on cognition and driving performance
Bibliographic record
Abstract
Research that has examined the influence of depression symptoms and antidepressant \nmedications on driving performance has revealed inconclusive findings (Brunnauer, Laux, \nGeiger, Soyka, & Moller, 2006; Bulmash et al., 2006; Ramaekers, 2003). The purpose of the present study was to elucidate the influence of depression symptoms and antidepressant medications on cognition and driving performances using self-report measures as well as an ecologically valid method measure, a driving simulator, and a clinical population. Two hundred and thirty-three drivers ranging in age from 18 to 35 years {M= 21.88; SD = 3.90 years) completed a screening measure that examined depressive and anxious symptoms, medication use, and self-reported driving behaviour on the Driving Behaviour Questionnaire (DBQ). Forty-three participants ranging in age from 18 to 35 {M= 24.24; SD = 5.05 years) also attended a laboratory session and completed a series of questionnaires designed to measure depression \ndriving habits, cognitive psychomotor functioning, and a diagnostic measure of MDD, two computerized tasks (one to measure attention and one to assess processing speed), and a 45 min simulated drive. In the overall sample, twenty-four (10.2%) participants were taking at least one antidepressant. Mean scores for depressive symptoms {M= 11.09; SD = 9.87) fell in the minimal range on the Beck Depression lnventory-11 (BDI-Il). A shortened version of the DBQ was created using this younger Canadian sample and correlation coefficients between the short and long version were excellent, ranging from .91 to .94. Overall, depressive symptoms and antidepressant use displayed little relationship to self-reported driving behaviour or driving performance on the driving simulator. However, our results do suggest that age (B= .12) and the cognitive/affective (B = .12) impairments on the BDI-II are statistically significantly related to increased self-reported absent-minded driving behaviour {p = .03). Overall depressive symptoms {B = -2.48) and cognitive/affective {B = 3.45) impairments were also related to inattention on a \ncomputerized task measuring attention {p < .05). The cognitive and affective impairments in depression were also positively related to visual perceptual ability {B = 2.02). The overall patterns of self-report data, neuropsychological data, and behavioural data suggest that although there is some consistency between self-report measures and neuropsychological data, this does not necessarily mean these impairments in attention translate into actual driving impairments on the simulator. Future studies could conduct a similar study using on-road performance as the behavioural measure of driving performance.
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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 | 0.000 | 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".