The combined effects of on-road and simulator training with feedback on older drivers' on-road performance: Evidence from a randomized controlled trial
Bibliographic record
Abstract
OBJECTIVE: A number of training programs that seek to improve driving performance among older drivers are available accompanied by a growing interest in their effectiveness. The purpose of the present investigation was to examine the combined effect of (1) basic in-class training (BT); (2) on-road training with individualized feedback (OR); and (3) training on a driving simulator (S). METHODS: Using a randomized controlled trial study design, 78 older drivers were randomly assigned to one of 3 groups (BT, BT + OR, or BT + OR + S). All participants completed a pre- and postintervention on-road driving evaluation on a standardized route. The driving evaluations were recorded using video and Global Positioning System (GPS) equipment and were scored by a blind assessor. RESULTS: The results indicated a significant reduction of approximately 30% in overall number of driving errors/omissions among participants in the BT + OR and the BT + OR + S groups in comparison to participants in the BT group. CONCLUSIONS: This study adds to the mounting evidence demonstrating the effectiveness of individualized driver training in improving safe driving among older adults.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".