A Profile of Adolescents who Attend DriverEducation for the Insurance Discount:Are Insurers Rewarding Bad Risks?
Notice bibliographique
Résumé
Problem: The overrepresentation of adolescent drivers in crashes is a robust phenomenon. Driver education (DE) is a popular countermeasure, and in most North American juridictions, insurers grant automobile insurance premium discounts to DE graduates. However, over the past 20 years, evaluations have consistently demonstrated that DE does not reduce, and may even increase, crash risk among adolescent novice drivers. Providing premium reduction incentives to DE graduates may possibly increase crash risk in two ways. One, by reducing the overall cost of licensing and car ownership, insurance may increase driving exposure. Two, insurance may increase morale hazard, a careless attitude toward prevention. The human and financial losses resulting from adolescent crashes are a serious problem for public health and for insurers. Insurance is also known to increase moral hazard, a tendency to make dishonest claims - losses due to fraud are a significant problem for insurers. Therefore, the DE insurance discount may not be optimally efficient for reducing insurers' losses or for improving the public health. One approach to invesligating the effects of the DE insurance discount is to study the characteristics and the driving records of adolescents who are insurance-motivated, i.e. those who attend DE partly or entirely for the insurance discount. Method: A cohort of 1,804 novice drivers 16- to 19-years of age of both sexes completed an extensive questionnaire on learning methods, including motivation to attend or not to attend DE, risk taking, and lifestyles. Questionnaire data were linked on an individual basis with government records on exam performance, violations, and crashes. Among the participants who attended DE (N = 1,536), the importance of the insurance discount in their motivation to attend DE was studied in relation to violation and crash records during the first 450 days of unsupervised driving and explanatory variables from the questionnaire. Results: Insurance-motivated participants, compared to those who were not motivated by the insurance discount, were more likely to have: greater violation risk, more tolerant attitudes towards speeding and risk taking, and less financial support from family for all licensing and driving related expenses. Insurance motivation was also associated with the likelihood of presenting fraudulent DE certificates and expressing a willingness to defraud insurance companies. Discussion: Increased violation and crash risk associated with insurance-motivation may possibly be due to greater morale hazard. The data also indicate thar insurance motivation may be associated with greater moral hazard and potential future losses for insurers. Alternative methods for insuring adolescent drivers are suggested with the aim of decreasing insurance losses and injury risk by attempting to decrease both morale and moral hazard.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».