Seasonal variation in older adults’ driving trip distances
Bibliographic record
Abstract
To date very few studies have examined the difference in driving patterns between winter and non-winter driving and those that have, have primarily used self-report. The purpose of this study was to determine if there were changes in trip distance between winter/non-winter and inclement/non-inclement driving in older adults using a sub-set of Candrive participants. Candrive is a longitudinal study examining the everyday driving patterns and habits of older drivers. Participants were recruited from seven different sites in Canada (Ottawa, Toronto, Montreal, Hamilton, Thunder Bay, Winnipeg, and Victoria). In total 279 participants (of which 248 were kept for analyses of City Only Trips) were included for analysis, almost 50% were female, with an average age at enrolment of 77.5 ± 5.2 years. A total of 377,464 trips were taken on 866 different days. It was found that there was a 7% decrease in trip distance during winter when controlling for day and site when examining all trips taken by older drivers. In addition, there was a 1% decrease in trip distance during winter and a 5% increase in trip distance during rain when compared to no precipitation when controlling for precipitation type (or winter respectively), day, and site, when only looking at trips in the city. There was a minimal (albeit significant) change in trip distance associated with both winter and inclement weather conditions, suggesting that older drivers may not be adjusting their driving patterns during these conditions as much as was previously thought based on the self-report literature.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".