Clinical Utility of the DriveFocus™ Intervention on Young Drivers With and Without Experience
Bibliographic record
Abstract
Introduction. The DriveFocus™ intervention addresses the ability of young drivers to detect and respond to critical roadway information. DriveFocus is an interactive video-based tablet application that teaches users how to detect and prioritize critical roadway items. However, young drivers with and without experience may respond differently to the intervention. Objectives. We compared the simulated driving performance of two 17 year-old licensed drivers with (novice) and without (learner) driving experience, after three (post-test 1) and six (post-test 2) intervention sessions. Methods: We collected clinical, driving performance (number of visual scanning, adjustment to stimuli, and total driving errors) and acceptability data. Results. The learner driver made more visual scanning, adjustment to stimuli and total errors when compared to the novice. Both participants exhibited a decrease in both types and number of driving errors from baseline to post-test 2 and the learner also made less driving errors at post-test 1. Both participants rated the perceived ease of use of the intervention favorably. Conclusions: This study lays the foundation to examine the impact of the DriveFocus™ intervention among novice and more experienced young drivers.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".