Prevalence of at-risk drinking among Brazilian truck drivers and its interference on the performance of executive cognitive tasks
Bibliographic record
Abstract
BACKGROUND: Binge drinking (BD) has been associated with an increase in the risk of alcohol-related injuries. Alcohol continues to be the main substance consumed by truck drivers, a population of special concern, since they are often involved in traffic accidents. The aim of this study was to estimate the prevalence of BD and its interference in the executive functioning among truck drivers in Sao Paulo, Brazil. METHODS: A non-probabilistic sample of 684 truck drivers was requested to answer a structured research instrument on their demographic data and alcohol use. They performed cognitive tests to assess their executive functioning and inventories about confounding variables. The participants were then divided according to their involvement in BD. RESULTS: 17.5% of the interviewees have reported being engaged in BD. Binge drinkers showed a better performance on one test, despite having done so at the expense of more mistakes and lower accuracy. More interestingly, binge drinkers took three seconds longer than non-binge drinkers to inhibit an inadequate response, which is worrisome in the context of traffic. Overall, the deleterious effect of BD on performance remained after controlling for the effects of confounding variables in regression logistic models. CONCLUSIONS: As the use of alcohol among truck drivers may be as a way to get by with their work conditions, we believe that a negotiation between their work organization and public authorities would reduce such use, preventing negative interferences on truck drivers' cognitive functioning, which by its turn may also prevent traffic accidents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".