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Record W6979736365

The acute effects of low dose of alcohol on simulated driving performance on young male and female drivers with high versus low testosterone levels

2016· dissertation· en· W6979736365 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2016
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiological Activity of Diterpenoids and Biflavonoids
Canadian institutionsnot available
Fundersnot available
KeywordsSensation seekingTestosterone (patch)ImpulsivityInjury preventionPoison controlHuman factors and ergonomicsYoung adultSuicide prevention
DOInot available

Abstract

fetched live from OpenAlex

Background: According to the World Health Organization, road traffic crashes represent the single leading cause of death for young people. Road traffic crashes are largely preventable, yet the precise mechanisms underlying individual driving risk remain relatively unidentified. Some risky drivers are over-represented among those involved in serious and fatal road traffic crashes, including those who misuse alcohol. Understanding what drives their risk taking is a key to developing evidence-based and effective prevention programs. Research focusing on the causes of risky driving has examined neurobiological markers of risk taking, such as cortisol, serotonin, and adrenaline. The steroid hormone testosterone has received substantial attention in relation to risk taking, but not specifically related to driving behaviour. Testosterone has been associated with sensation seeking and impulsivity, two consistent correlates of risky driving. Furthermore, interactions between testosterone and alcohol in relation to sensation seeking and impulsivity have been observed, potentially making testosterone a specific factor in driving while impaired with alcohol. In this experiment, four hypotheses were tested: H1) Young drivers with higher testosterone level show higher mean speed in simulation than young drivers with lower testosterone level; H2) Alcohol moderates the relationship between testosterone level and mean speed; H3) Sex moderates the relationship between testosterone level and mean speed; H4) Sensation seeking and impulsivity mediate the relationship between testosterone level and mean speed under alcohol. Methods: This study is part of a larger between-subject, randomized, placebo-controlled experiment. Eleven male and eleven female drivers aged 18-34 years were recruited and their saliva assayed for endogenous testosterone level using radioimmunoassay. Participants also responded to questionnaires assessing sensation seeking and impulsive personality characteristics, and performed two driving simulation tasks, one at BAC (blood alcohol content) of 0% (no-alcohol condition) and another at a positive BAC of 0.02% - 0.05% (i.e., alcohol condition). Results: H1 was not supported by the findings; young drivers with higher testosterone level did not report higher simulated speed than young drivers with lower testosterone level, p > .05. H2 was supported. A significant alcohol X testosterone interaction was detected (p = .04; partial Eta-squared = .20). Specifically, young drivers with higher testosterone level reported higher mean speed than drivers with lower testosterone level, but only after alcohol consumption. Sex was not a significant moderator of the relationship between testosterone level and simulated speed (p > .05). Neither sensation seeking nor impulsivity mediated the relationship between testosterone level and simulated speed under alcohol (p > .05).Conclusion: Interaction between testosterone hormone and alcohol may be one mechanism contributing to risky driving under legal BAC (in Canada < 0.08%). Practically, findings from this study can help future development of educational programs, based on age and sex, to better inform young drivers about neurobiological factors that can affect their ability to drive.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.230
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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