Factors Influencing Transitions from Driver to Non-Driver: Evidence from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Abstract Driving enables older adults to maintain independence and community mobility. Driving plays a pivotal role in the ability to engage in activities, socialize, run errands, and access health care services; yet many people eventually stop driving. This study investigates factors that contribute to transitions from driver to non-driver (i.e., driving status) using data from the Canadian Longitudinal Study on Aging (CLSA). Among participants aged 45–85 who reported driving at baseline ( n = 30,901), 1.65 percent ( n = 510) had stopped driving at follow-up (three years later). Logistic regression identified predictors of this transition, including older age, female sex, lower income, urban residence, poorer self-rated health, difficulties with activities of daily living, low memory scores, and vision problems. These findings highlight the interplay of physical, cognitive, and environmental factors in driving cessation. This research advances understanding of mobility transitions in later life and informs targeted strategies to support older adults as they plan for driving retirement.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".