ASSESSMENT OF THE EFFECTIVENESS OF A COMPREHENSIVE DRIVING TRAINING PROGRAM FOR OLDER ADULT DRIVERS
Bibliographic record
Abstract
Several driving retraining programs have been developed to improve older drivers’ driving skills and researchers have been interested in assessing their effectiveness. This study was designed as a replication of a previous study done by our team (Sawula et al. submitted). The objective of both studies was to determine if a compressive training program combining 1) basic in-class training (BT); 2) on-road training with individualized feedback (OR); and 3) on-road training with individualized feed-back plus training on a driving simulator (ORS) would lead to improvements in older drivers’ on-road driving evaluations. Using a randomized controlled trial (RCT) study design, 43 older drivers (mean age=71.7 years, SD = 4.91) were randomly assigned to one of three groups (BT, BT+OR, or BT+ORS). All participants completed a pre- and post-intervention on-road driving evaluation on a standardized route. The driving evaluations were recorded using video and GPS equipment and were scored by a blind assessor. The results of this study demonstrated that post-intervention driving evaluation scores for the BT+OR and BT+ORS groups when compared to the BT group were significantly different. While unsafe driving errors showed a 6% reduction in the BT group, BT+OR and BT +ORS group reduced their errors per 23% and 34% respectively. There were no differences between BT+OR and BT+ORS group, thus further research is required to determine the contribution of simulator training on its own.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".