The Association between Psychological Resilience and Driving Behavior among Older Drivers in Australia
Bibliographic record
Abstract
This study compared a sample of Australian drivers aged 77 years and older to participants from an older driver longitudinal cohort study (Ozcandrive) and examined the relationship between resilience and self-reported driving measures within these samples. Using a survey with a subset of questions from Ozcandrive, data were collected from 237 older drivers throughout Australia. The two samples were analyzed for differences in demographics, health, resilience, and self-reported driving behavior. A series of multiple regression models were fit for each driving outcome measure for both samples. The two samples had both similarities and differences, with the largest difference observed for resilience. Strong and consistent associations were found between resilience and driving comfort, abilities, and frequency for the Australian sample. Across samples, resilience remained a significant variable in seven of 10 regression models, more than any other independent variable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".