Multiple-Session Simulator Training for Older Drivers and On-Road Transfer of Learning
Bibliographic record
Abstract
Driving retraining classes may offer an opportunity to attenuate some of the aging manifestation that may alter driving skills. Unfortunately, there are suggestions that classroom programs do not allow to improve the driving performance of elderly drivers. The aim of this study was to evaluate if specific simulator training sessions with video-based feedback can modify on-road behaviors of elderly drivers. In order to evaluate the effectiveness of the training, 10 elderly drivers who received feedback were tested before and after the training program with an on-road standardized evaluation. A control group (12 older drivers) also participated. Participants in this group received a classroom training program and similar exposure to driving in a simulator but without drivingspecific feedback. After attending the training program, the control group showed no modification of their driving performance (on-road score, frequency of successful turning maneuvers and frequency blind spot verification before lane change maneuvers). On the other hand, participants in the feedback group improved their driving skills for all maneuvers that were evaluated. These results suggest that simulator training transferred effectively to on-road performance. In order to be effective, driving programs should include active practice sessions with driving specific feedback.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".