THE RELATIONSHIP BETWEEN OLDER DRIVERS’ RESILIENCE AND SELF-REPORTED DRIVING MEASURES OVER 5 YEARS
Bibliographic record
Abstract
Abstract As people age into older adulthood, they are more likely to experience events that impact their driving, such as age-related cognitive and functional declines, serious illness, or disability. The ability to demonstrate resilience following such adversity may influence one’s decisions and feelings about driving. This study investigated whether resilience of older drivers changes over time, and if relationships between resilience, gender, and self-reported driving-related abilities, perceptions and practices remain stable or change. Participants were from the Candrive/Ozcandrive study, a prospective cohort study of older drivers from Canada, Australia and New Zealand. Analyses are presented from a subset of Ozcandrive participants (n=125) from Australia who completed a resilience scale at two time points approximately five years apart, as well as measures of driving comfort during the day and night, perceived driving abilities, and driving frequency. Participants were primarily male (67.2%) with a mean age of 81.6 years (SD=3.3, Range=76.0-90.0) at Time 1. Resilience increased significantly from Time 1 to Time 2 (Median=82.0/84.00, z=-2.9, p<.01). Although females had significantly higher resilience than males at both Time 1 (Median=84.0/81.0, U=2.3, p=.02) and Time 2 (Median=86.5/82.0, U=2.1, p=.03), there was a statistically significant increase in resilience of males over five years (p<.01) and no statistical change for females. Results show small but significant positive correlations, and increasingly stronger relationships over time between older drivers’ resilience and driving comfort as well as perceived driving abilities. Future research will use modelling to examine the association of various factors on the change in resilience and driving-related measures.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".