Older Driver Estimates of Driving Exposure Compared to In-Vehicle Data in the Candrive II Study
Bibliographic record
Abstract
OBJECTIVE: Most studies on older adults' driving practices have relied on self-reported information. With technological advances it is now possible to objectively measure the everyday driving of older adults in their own vehicles over time. The purpose of this study was to examine the ability of older drivers to accurately estimate their kilometers driven over one year relative to objectively measured driving exposure. METHODS: A subsample (n = 159 of 928; 50.9% male) of Candrive II participants (age ≥ 70 years of age) was used in these analyses based on strict criteria for data collected from questionnaires as well as an OttoView-CD Autonomous Data Logging Device installed in their vehicle, over the first year of the prospective cohort study. RESULTS: Although there was no significant difference overall between the self-reported and objectively measured distance categories, only moderate agreement was found (weighted kappa = 0.57; 95% confidence interval, 0.47-0.67). Almost half (45.3%) chose the wrong distance category, and some people misestimated their distance driven by up to 20,000 km. Those who misjudged in the low mileage group (≤5000 km) consistently underestimated, whereas the reverse was found for those in the high distance categories (≥ 20,000); that is, they always overestimated their driving distance. CONCLUSIONS: Although self-reported driving distance categories may be adequate for studies entailing broad group comparisons, caution should be used in interpreting results. Use of self-reported estimates for individual assessments should be discouraged.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".