Older Drivers: Attention, Habits and Memory
Bibliographic record
Abstract
Abstract Older drivers are at increased risk for negative outcomes and generating knowledge to promote safe driving and when to consider retirement from driving are of the utmost importance. This study evaluated how demographics and cognitive state are related to driver habits and attention. A sample of community dwelling older adults (n = 111) without global cognitive impairment on the Minnesota Cognitive Acuity Screen completed baseline assessments before the initiation of a driver safety intervention. Participants reported as 61% female, mean age 69.1 (SD 6.2), 15.9 mean years education (SD 2.4), and 66% retired. 19% reported living in an urban setting, 47% suburban, and 34% rural. Self-reported measures included the Driving and Riding Avoidance Scale (DRAS), Cognitive Failures Questionnaire - Driving (CFQ), Attention-Related Driving Errors Scale (ARDES), Mindfulness Attention Awareness Scale (MAAS), and Aggressive Driving Behavior Questionnaire (ADBQ). No measure was significantly related to participant demographics. Brief performance measures of cognition administered by telephone, including the Montreal Cognitive Assessment (MoCA) Blind and oral Trails A and B, were mostly uncorrelated with the self-reported driving and attention measures. One exception was the MoCA word recall and DRAS, r(106) = -.224, p=.02, in which poorer memory was associated with greater driver avoidance. These findings are noteworthy for 1) a lack of correlation between self-reported measures of driver attention and either age or cognition, 2) no gender differences found in measures of driver behavior, and 3) in a sample pre-screened for cognitive impairment, the word recall was significantly related to older adults’ self-reported driver avoidance behaviors.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".