Evaluating Older Drivers in Their Everyday Driving Environments
Bibliographic record
Abstract
Abstract Developing tools that accurately detect at-risk driving behaviors is a public-health priority. There is a need for a measure that accurately assesses older drivers’ level of competence on familiar roadways. The objective of this presentation is to describe the development of the procedures and scoring of a new approach, the Electronic Driving Observation Schedule (eDOS), to observe everyday driving in the community. The eDOS was used to record and compare the driving environment and performance of older drivers and low-risk younger drivers during their everyday driving. Older (n=160, >74y) and younger (n=60, 35-64y) drivers completed a 20-30-minute drive from their home to destinations of their choice. Older drivers drove on simpler routes with fewer intersections and lane changes. Both groups made few driving errors, which were mostly low-risk. Younger drivers tended to demonstrate poor driving habits (not signaling, speeding, poor lane position) and compliance with road rules. Part of a symposium sponsored by Transportation and Aging Interest Group.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".