Health conditions, health symptoms and driving difficulties in older adults
Bibliographic record
Abstract
OBJECTIVES: Previous research has indicated that age-related medical or health conditions can affect driving performance in older adults but little, if any, research has examined the mechanisms through which health conditions affect driving difficulties in older adults. DESIGN: Cross-sectional, correlational study. SETTING: Random sample from the community. We examined the nature of the relations among health conditions, health-related symptoms, physical fitness levels and specific types of self-reported driving difficulties in a random sample of older adults. PARTICIPANTS: Three hundred eighteen adults 60 years of age or older. INTERVENTION: None. MEASUREMENTS: General health, health-related symptoms, driving-related difficulties and physical activity. RESULTS: Our findings support the position that health-related symptoms are more clearly associated with driving difficulties than are health conditions, and mediate the relations between health conditions and driving difficulties. Health-related symptoms involving the spine and lower body appeared to be particularly relevant to difficulties with driving experienced in those body areas (i.e. spine and lower body). CONCLUSION: These findings are encouraging, in that the most frequently reported symptoms are in areas highly amenable to modification and, in that most of our respondents indicated a willingness to engage in exercise if an association between fitness and driving was demonstrated.
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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 | 0.003 |
| 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 | 0.000 |
| 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".