Examining the Driving Habits of Older Canadians as Captured by the COMPASS‐ND Study
Bibliographic record
Abstract
Abstract Background Driving is associated with a sense of autonomy and good quality of life in older adults. However, older adults often change their driving habits, such as regulating how often, how far, and under what conditions (e.g., time of day, weather) they drive, in part due to changes in cognition. Our study describes data from the first large‐scale Canadian cohort study to investigate differences in driving habits across cognitive groups, including cognitively healthy older adults (control), Subjective Cognitive Decline (SCD), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD). Method We analyzed data from the Comprehensive Assessment of Neurodegeneration and Dementia study (COMPASS‐ND, Chertkow et al., 2019), a population‐based study of Canadians with various cognitive and functional abilities (May 2021 Data Release). The final sample analyzed included 317 participants (55% Females, Meanage = ∼72). We report the responses for the following driving‐related questions: licensing status (no longer have license, drive with or without restrictions), driving frequency, driving distance, and driving restrictions (e.g., bad weather). Result Most participants across all groups reported that they have a driver’s license without restrictions, including the AD group (AD: ∼61%, remaining groups: >90%). The following analyses only included current drivers. In terms of the frequency of driving and the distance travelled, as expected, the AD group drove the least often and the shortest distances. The most frequent restriction types selected by all groups were bad weather and nighttime driving. The number of restriction types increased with the severity of cognitive decline. Interestingly, driving restrictions reported by the SCD group more closely approximated the cognitively impaired groups (MCI and AD) than the control group. Conclusion Although many of the AD group participants still had a license, they also reported the greatest number of driving restrictions. Further, the SCD group and MCI group were quite similar in their responses for several items, indicating that even SCD may be associated with differences in driving habits. Implications of driving‐related limitations include reduced independence, socialization, participation, and access to resources (e.g., medical care, groceries).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| 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".