Communicating increased driving risk: A study of mature driver expectations
Bibliographic record
Abstract
BACKGROUND: Driving is essential for maintaining mobility, social participation, and independence, yet it relies on cognitive and physical abilities that may decline with age, increasing risks for older adults. Drivers aged 65 and older experience disproportionately higher rates of collisions and fatalities compared to middle-aged drivers. Many struggle with decisions about adjusting their driving habits or retiring but rarely use existing guidelines. Additionally, few older drivers assess their abilities before mandatory licensing tests, leaving them unaware of potential risks. METHOD: This study explores the needs of older drivers to help them understand their collision risks and plan for driving retirement. A nationwide survey of Canadian drivers aged 55+ was conducted with ethics approval from Carleton University and the Bruyère Health Research Institute. The survey received 386 responses (average age: 75) and examined driving habits, perceptions of vehicle data collection, and preferences for receiving feedback on driving safety. RESULT: Three main themes emerged from the data: empowerment, privacy, and personalization. Results showed that 65% of drivers already use in-vehicle safety technologies, but half required more information before adopting new tools. Participants expressed interest in features like driving behaviour reports, personalized safety tips, and historical driving trends. Privacy and security concerns (68%) and technology accuracy (52.4%) were barriers to adoption, while ease of use (65%), strong privacy protections (56.4%), and proven reliability (55.9%) were key motivators. Respondents preferred to receive feedback on demand (50.4%) or monthly (32.2%) via email (53%) or app-based platforms (45.2%). Most participants preferred sharing feedback with spouses (51.6%), while fewer were comfortable sharing it with children (35%) or healthcare providers (16.2%). CONCLUSION: These findings suggest that driving feedback technologies could help older drivers monitor their abilities and engage in evidence-based discussions with healthcare providers. Objective data enables healthcare providers to address sensitive topics, like declining driving ability, with actionable insights, easing emotional challenges. Prioritizing empowerment, privacy, and personalization ensures trusted, user-focused tools that support self-monitoring, informed decisions, and collaboration, promoting safer driving and sustained independence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".