Older Drivers Reduced Engagement in Distracting Behaviors Over a Six-Year Period: Findings From the Candrive Longitudinal Study
Bibliographic record
Abstract
OBJECTIVES: Baltes and Baltes' "selective optimization with compensation" model is pertinent to driving but evidence about the use of compensation using longitudinal designs is scarce. Therefore, we sought to determine if older drivers reduced their engagement in distracting behaviors while driving, over a 6-year period. METHODS: We used data captured over several annual assessments from a cohort of 583 drivers aged 70 and older to determine if their engagement in 12 distracting behaviors (e.g., listening to the radio, talking with passengers) declined over time. We adjusted our multivariable model for several potential confounders of the association between our outcome variable and time. RESULTS: Overall, and after adjustment for potential confounders, the participants reduced their engagement in distracting behaviors over the study period (odds ratio [OR] = 0.96, 95% confidence interval [CI] = 0.95-0.97). Baseline age was negatively associated with engagement in distracting behaviors (OR = 0.95, 95% CI = 0.94-0.96). Men engaged in more distracting behaviors than women (OR = 1.15, 95% CI = 1.03-1.27), as did participants living in the largest urban centers compared to participants living in the smallest areas (OR = 1.21, 95% CI = 1.04-1.41). The number of kilometers driven per year (for every 10,000 km) was positively associated with the proportion of distracting behaviors drivers engaged in (OR = 1.13, 95% CI = 1.08-1.19). DISCUSSION: Drivers in our cohort reduced their engagement in distracting behaviors over the study period. This suggests that older drivers adjust their driving over time, which aligns with age-related theories and models about compensation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 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".