Mitigating Teen Driver Distraction: In-Vehicle Feedback Based on Peer Social Norms
Bibliographic record
Abstract
OBJECTIVE: To investigate the efficacy of in-vehicle feedback based on peer social norms in mitigating teen driver distraction. BACKGROUND: Distraction is a significant problem among teen drivers. Research into the use of in-vehicle technologies to mitigate this issue has been limited. In particular, there is a need to study whether social norms interventions provided through in-vehicle feedback can be effective. Peers are important social referents for teens; thus, normative intervention based on this group is promising. Socially proximal referents have a greater influence on behavior; thus, tailoring peer norm feedback based on gender may provide additional benefits. METHOD: In this study, 57 teens completed a driving simulator experiment while performing a secondary task in three between-subject conditions: (a) postdrive feedback incorporating same-gender peer norms, (b) postdrive feedback incorporating opposite-gender peer norms, and (c) no feedback. Feedback involved information based on descriptive norms (what others do). RESULTS: Teens' self-reported frequency of distraction engagement was positively correlated with their perceptions of their peers' engagement in and approval of distractions. Feedback based on peer norms was effective in reducing distraction engagement and improving driving performance, with no difference between same- and opposite-gender feedback. CONCLUSION/APPLICATION: Feedback based on peer norms can help mitigate driver distraction among teens. Tailoring social norms feedback to teen gender appears to not provide any additional benefits. Longer-term effectiveness in real-world settings should be investigated.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".