Low uptake of driver refresher courses by older adults: An examination of potential explanatory variables using the Candrive cohort
Bibliographic record
Abstract
Older drivers benefit from driver refresher courses, particularly courses with on-road training. Yet, the uptake of these courses and the factors associated with taking them is poorly documented. We used data from the Candrive prospective cohort (N = 928) to examine these issues using variables representing sociodemographic factors (e.g., age), health-related factors (e.g., medical conditions), and driving-related factors (e.g., driving comfort). The outcome variable was operationalized as having taken a non-mandatory refresher course in the last 10 years or never. Participants’ mean age was 76.21 (SD = 4.85) and 576 (62.1 %) were males. Ninety-eight participants (10.6 %) reported having taken a non-mandatory refresher course within the last 10 years, and less than half of those reported that it included an on-road component. Only nine percent of participants had discussed driving with a physician. The multivariable regression model (N = 746; −2 log likelihood = 515.10, p < .001, Nagelkerke R 2 = 0.12) identified five variables as statistically significant. The odds of having taken a course were higher with age (OR = 1.53, 95 % CI = 1.23, 1.92), when one’s driving was perceived as important for others (OR = 1.87, 95 % CI = 1.26, 2.80), and for participants who spoke to their family (OR = 1.80, 95 % CI = 1.04, 3.09) or to a physician about driving (OR = 2.24, 95 % CI = 1.15, 4.36); the odds were lower for those who benefited the most personally from driving (OR = 0.64, 95 % CI = 0.51, 0.82). The uptake of driver refresher courses is low and few discussions about driving took place with physicians. Further research is needed to understand the barriers and facilitators related to older drivers’ involvement in refresher courses. • Among a sample of Canadian older drivers, less than 30 % reported having ever taken a non-mandatory refresher course. • Age and the importance of driving are associated with the odds of having taken a refresher course. • Few drivers discussed driving with family/physicians, but these discussions are associated with taking a refresher course.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".