The Combination of Two Training Approaches to Improve Older Adults' Driving Safety
Bibliographic record
Abstract
OBJECTIVE: An increasing number of older adults rely on the automobile for transportation. Educational approaches based on the specific needs of older drivers may help to optimize safe driving. We examined if the combination of an in-class education program with on-road education would lead to improvements in older drivers' knowledge of safe driving practices and on-road driving evaluations. METHODS: We used a multisite, randomized controlled trial approach. Participants in the intervention group received the in-class and on-road education; those in the control group waited and were offered the education afterwards. We measured knowledge of safe driving practices before and after the in-class component of the program and on-road driving skills before and after the whole program. RESULTS: Participants' knowledge improved from 61% of correct answers before the in-class education component to 81% after (p < .001). The on-road evaluation results suggested improvements on some aspects of safe driving (e.g., moving in roadway, p < .05) but not on others. CONCLUSIONS: The results of this study demonstrate that education programs focused on the needs of older drivers may help improve their knowledge of safe driving practices and actual driving performance. Further research is required to determine if these changes will affect other variables such as driver confidence and crash rates.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".