Mitigating Young Drivers’ Engagement in Distractions: Role of Emotions
Bibliographic record
Abstract
Distracted driving is a critical issue, particularly among young drivers who display higher rates of cellphone use while driving, making them more susceptible to distraction-related crashes. This dissertation investigates the effectiveness of emotion-based interventions, specifically targeting guilt, shame, and fear, to mitigate cellphone-related risks among young drivers. By complementing fear-based approaches with other emotions, it explores the potential for more impactful interventions in curbing distracted driving behaviours. The research comprises three studies (two surveys and one driving simulator experiment) conducted among young drivers (aged 18 to 25) in Ontario, Canada. The first study focused on examining the association between anticipated guilt, shame, and fear, and intention to engage in distracted driving. Utilizing an extended Theory of Planned Behaviour (TPB) model (N=99 out of a sample of 403), the results revealed that anticipating feelings of guilt, shame, and fear negatively predict distraction engagement, surpassing standard TPB constructs. Additionally, the study (N=403) compared potentially emotion-evoking road signs ("children crossing" and "my mom/dad works here") to generic road signs ("pedestrian crossing" and "workers ahead"). The findings demonstrated that in scenarios where emotional responses were expected, changes in anticipated emotions were inversely associated with changes in intention to engage in cellphone distractions. Building upon these findings, the second survey study (N=305) compared the effectiveness of three different anti-distracted driving videos: (1) informational, (2) fear-based, and (3) guilt, shame, and fear (GSF) targeting approaches. The results revealed that targeting guilt and shame alongside fear lead to a higher likelihood and extent of willingness to reduce engagement in cellphone distractions, outperforming fear-based and informational approaches. The final study employed a driving simulator to investigate the impact of incorporating guilt and shame in fear-based interventions. Participants (N=36) were assigned to one of three conditions: control (no-intervention), fear-based, or GSF approach. Pre- and post-experiment questionnaires, and 4 experimental drives (1 baseline and 3 subsequent intervention drives), were conducted. The same anti-distracted driving videos used in the second survey study were shown to the participants (in intervention conditions) after the baseline drive. Driving performance measures, eye-tracking data, and questionnaire responses were evaluated. Those exposed to the GSF approach demonstrated reduced engagement in secondary tasks while driving, highlighting the potential effectiveness of appealing to moral and emotional aspects of distracted driving. In conclusion, this dissertation underscores the significance of considering emotions, including guilt and shame, alongside fear-based approaches when designing targeted interventions to reduce distracted driving among young drivers. By appealing to emotions, interventions can foster a stronger motivation for behaviour change, contributing to enhanced road safety.
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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 | 0.001 | 0.005 |
| 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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".