Examining the effects of music on visual attention and driving behaviors in male and female adolescents
Bibliographic record
Abstract
OBJECTIVE: Motor vehicle crashes (MVCs) are a leading cause of injury-related deaths for youth 16-19 years and males experience significantly more of these events than females. This study examined how in-car listening to their favorite music influences visual attention and risky driving behaviors among males and females 17-19 years of age. METHOD: An immersive driving simulator was used to automatically measure a variety of performance indicators and video recordings provided data on attention and one-handed driving. Participants completed two 25-minute drives in which hazards unexpectedly appeared, listening to their playlist of favorite music over the radio during one of these. RESULTS: For both males and females, visual attention to the road was reduced significantly when driving with music playing. With regard to driving performance, there were no sex differences or effect of music on driving speed and hazard reaction time. For both males and females, steering performance was improved when listening to music. However, music influenced one-handed driving differentially based on sex of the driver. Males, but not females, engaged in more one-handed driving when listening to music, and this was associated with males hitting more hazards. CONCLUSION: Generally, driving with music playing poses more risk to male than female teen 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 | 0.000 | 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) | 0.002 | 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".