Personal and Clinical Factors Associated with Older Drivers’ Self-Awareness of Driving Performance
Bibliographic record
Abstract
Most older adults perceive themselves as good drivers; however, their perception may not be accurate, and could negatively affect their driving safety. This study examined the accuracy of older drivers' self-awareness of driving ability in their everyday driving environment by determining the concordance between the perceived (assessed by the Perceived Driving Ability [PDA] questionnaire) and actual (assessed by electronic Driving Observation Schedule [eDOS]) driving performance. One hundred and eight older drivers (male: 67.6%; age: mean = 80.6 years, standard deviation [SD] = 4.9 years) who participated in the study were classified into three groups: underestimation (19%), accurate estimation (29%), and overestimation (53%). Using the demographic and clinical functioning information collected in the Candrive annual assessments, an ordinal regression showed that two factors were related to the accuracy of self-awareness: older drivers with better visuo-motor processing speed measured by the Trail Making Test (TMT)-A and fewer self-reported comorbid conditions tended to overestimate their driving ability, and vice versa.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".