CHANGES IN DRIVER’S LICENSE STATUS AMONG MID-AGED AND OLDER CANADIANS OVER THREE YEARS
Bibliographic record
Abstract
Abstract For older people, driving may contribute to health and quality of life. Conversely, driving cessation is associated with negative outcomes, including poor physical and mental health. We examined changes in driving status over a three-year period among participants in the Canadian Longitudinal Study on Aging (CLSA), which includes Canadians aged 45-85 at baseline. At baseline (data collected between 2011 and 2015) and follow-up (data collected between 2015 and 2018), participants reported whether they had a driver’s license. Using multiple logistic regression, we examined the relationship between covariates and changes in license status (i.e., having a license at baseline but not at follow-up vs. maintaining license). Of the participants who reported having a driver’s license at baseline (n=36,266), 1.19% (n=432) reported no longer having one at follow-up. This change was associated with lower income categories and poorer self-rated health. Age (B=.12, p<.001) and depression symptoms (B =.04, p<.001) were positively associated with no longer having a license. Participants who reported a change had lower scores on a memory task (Rey Auditory Verbal Learning Test; B=-.07, p<.001). Women had greater odds than men to report a change in driver’s license at follow-up (OR=1.5, p<.001). The results highlight the salience of health, cognition, and income as correlates of driving cessation in a sample of mid-aged and older adults. These results may help identify individuals who are likely to stop driving and who may need additional supports maintaining mobility, health, and quality of life.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".