Interactions between Alcohol and Cannabis Consumption and Cognitive Load on Simulated Driving Measures
Bibliographic record
Abstract
Cognitive distraction, alcohol use, and cannabis use are all known to detrimentally affect driving performance. However, there is limited research to date examining the impact of a combination of these factors on driving. Using data from a recent clinical trial examining the effects of cannabis and alcohol use, separate and combined, on simulated driving, with a particular interest in the potential effects of cognitive load, a post hoc analysis of the interaction of these effects was completed. A linear mixed effect model was utilized, which allowed the consideration of drug condition, presence of cognitive load, and practice effects as fixed effects, as well as intra-participant variability as a random effect. A drug × cognitive load interaction was identified in the overall mean speed performance measure. Cognitive load increased the overall mean speed in the alcohol-only and alcohol/cannabis conditions, but not the placebo or cannabis-only conditions. In addition, cognitive load was observed to increase straightaway SDLP. Several drug effects were replicated from the original analysis of the study data. This thesis provides insights into the interaction of cognitive distraction, alcohol use, and cannabis use in impairing driving performance, highlighting the importance for further research and policy development, especially in light of changes to drug use prevalence.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".