A Randomized Trial of a Comprehensive Training Process to Enhance Safe Driving in Older Adults
Bibliographic record
Abstract
In Canada, older adult driving exposure is increasing quite drastically. However, older adult \ndrivers have a higher motor vehicle collision fatality risk compared to younger age groups. Therefore, \nolder adult driver safety is an area requiring considerable attention. Using a randomized controlled trial \nstudy design, the present study investigated the effectiveness of a comprehensive training process to \nenhance safe driving in older adults. Based on their age and sex, participants (n=78), aged 65 years and \nabove, were block randomized to one of three driving training intervention groups: 1) in-class training \n(control); 2) in-class plus on-road training (with individualized feedback); and 3) in-class plus on-road \nplus simulator training (with individualized feedback). The main outcome measure was the number of \nunsafe-driving actions committed before and after receiving designated driving training interventions on a \nstandardized on-road driving evaluation, captured by video and GPS technology, and scored by a blinded, \nindependent rater. Driving knowledge and driving comfort data were also collected for all participants \nbefore and after receiving their designated interventions. Mean baseline total on-road driving scores were \nsimilar for intervention groups, averaging 129.78 (SD=29.87) for the control group, 128.48 (SD=20.15) \nfor the in-class plus on-road training group, and 127.73 (SD=24.24) for the in-class plus on-road plus \nsimulator training group. The control group achieved an average reduction of 7.18 (95% CI [0.11, 14.26]) \nunsafe-driving actions; the in-class plus on-road training group and the in-class plus on-road plus simulator-training group achieved an average reduction of 41.64 (95% CI [26.21, 53.29]) and 38.69 (95% \nCI [22.20, 52.16]) unsafe-driving actions, respectively, especially regarding vehicle control and \nobservation errors. Driving knowledge also significantly improved from 74.4% to 83.2% of questions \nanswered correctly before receiving the in-class training component to after receiving the in-class training \ncomponent; however, there were no significant differences between intervention groups in post- \nintervention driving comfort levels. The findings demonstrate that achieving considerable improvements \nin older adults? driving relies on on-road training, and that individualized feedback supplementation \nshould be the focus of more inquiry. Limitations and future research directions are also discussed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".