Awareness Tool for Safe and Responsible Driving (OSCAR): A Potential Educational Intervention for Increasing Interest, Openness and Knowledge About the Abilities Required and Compensatory Strategies Among Older Drivers
Bibliographic record
Abstract
OBJECTIVE: This pilot study aimed to verify the impact of the awareness tool for safe and responsible driving (OSCAR) on older adults' (1) interest, openness, and knowledge about the abilities and compensatory strategies required for safe driving; (2) awareness of changes that have occurred in their own driving abilities; and (3) actual utilization of compensatory strategies. METHODS: A preexperimental design, including a pretest (T0) and posttest (T1) 8 to 10 weeks after exposure to the intervention, was used with 48 drivers aged between 67 and 84. The participants had a valid driving license and drove at least once a week. RESULTS: Overall, the results demonstrate that OSCAR increased interest, openness, and knowledge about the abilities and compensatory strategies of older drivers (P <.01). After exposure to OSCAR, the majority of the participants confirmed that changes had occurred in at least one of their abilities. Moreover, half of the older drivers reported having started using 6 or more compensatory strategies. CONCLUSION: In summary, in addition to increasing older adults' interest, openness, and knowledge to discussion about driving, OSCAR also improved awareness of the changes that could negatively impact safe driving and enhanced utilization of compensatory strategies. While promoting safe driving and the prevention of crashes and injuries, this intervention could ultimately help older adults maintain or increase their transportation mobility. More studies are needed to further evaluate OSCAR and identify ways to improve its effectiveness.
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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 | 0.002 |
| 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 | 0.001 | 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".