Older Drivers in Australia: Trends in Driving Status and Cognitive and Visual Impairment
Bibliographic record
Abstract
OBJECTIVES: To investigate self-reported driving status within three Australian states; associations between demographic, health, and functional factors and driving status; and the extent to which remaining a driver in spite of cognitive and visual impairments varies as a function of sex. DESIGN: Secondary data analysis of a pooled data set. SETTING: Australian communities. PARTICIPANTS: Adults aged 65 to 103 (N=5,206) from the Dynamic Analyses to Optimise Ageing (DYNOPTA) project. DYNOPTA is a unique data set created through the harmonization and pooling of data across nine separate Australian longitudinal studies of aging conducted between 1990 and 2007 (N=50,652). MEASUREMENTS: Driving status, demographic characteristics, Mini-Mental State Examination score, visual acuity, physical activity, and occupation. RESULTS: Men and participants with higher-level occupations had greater odds of driving. Older age, more medical conditions, and poorer vision increased the odds of not driving. Persons who were divorced, widowed, or never married were at a greater risk than married adults of not driving. Descriptive analyses revealed a large proportion of men with probable visual or cognitive impairments who reported driving. Subsequent comparative analyses between the DYNOPTA sample and other published U.S. and Canadian data revealed lower proportions of current drivers among Australian women and those at older ages, although there were consistently lower proportions of drivers within Australia and Canada than in the United States. CONCLUSION: The rate of men with probable dementia or visual impairments who reported driving is of particular concern. Research and policy need to focus on evidence-based assessment of older drivers and development of appropriate interventions and programs to maintain the mobility and independence of older adults.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".