Efficacy, strengths, and limitations of in-vehicle feedback technology to reduce young drivers’ risk: Recent findings from the literature
Bibliographic record
Abstract
The main goal of this presentation is to describe the current state of research in regards to in-vehicle feedback technology aimed at young drivers. Young drivers have a higher crash risk worldwide than other age groups, and the first months after licensing are the most dangerous. Several countries have achieved important crash reductions over the past years that are associated with the implementation of graduated driver licensing programs. Provisions of these primary and secondary prevention programs includes older age at licensing and driving privileges provided gradually to young drivers, such as driving at night and with young passengers. Development of in-vehicle technology, such as feedback devices, now allows easier implementation of secondary and tertiary interventions aimed at young drivers. A number of randomized controlled trials have been published and results suggest the efficacy of in-vehicle feedback devices in reducing some indices of risky behavior, such as g-force events. Research has also identified several obstacles to deployment of these devices, including acceptance by both young drivers and their parents. Results of recent studies by our research group on efficacy (N = 160) and acceptance (N = 380) of in-vehicle devices in 18-24 year old drivers, and individual factors that influence these dimensions, will be presented in light of the current research. The main discussion will address the efficacy of in-vehicle feedback technology to reduce young drivers’ risk, its strengths, limitations, and obstacles to implementation in primary, secondary, and tertiary prevention programs.
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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 | 0.024 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".