A Profile of Adolescents who Attend DriverEducation for the Insurance Discount:Are Insurers Rewarding Bad Risks?
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".