Mitigating Young Drivers’ Engagement in Distractions: Role of Emotions
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».