FEW OLDER DRIVERS TAKE REFRESHER COURSES: IDENTIFICATION OF FACILITATORS AND BARRIERS
Bibliographic record
Abstract
Abstract Older drivers may benefit from driver refresher courses, particularly from courses with on-road training. Yet, the uptake of refresher courses and the factors associated with the decision to enroll is poorly documented. We examine these issues using the Candrive prospective cohort (recruitment stated in 2009; N = 927), a representative sample of older Canadian drivers. We operationalized our outcome variable as having taken any non-mandatory refresher course within the last 10 years (yes/no). We developed a conceptual model that includes sociodemographic factors (e.g., age), health-related factors (e.g., medical conditions), and driving-related factors (e.g., driving comfort), and identified the contribution of these factors using logistic regression. 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; roughly half reported the course included an on-road component. The multivariable regression model (-2 log likelihood = 518.41, p <.001, Nagelkerke R-square =.17) identified six of 13 variables entered into the model as statistically significant. The odds of having taken a course differed by province of residence, and was higher with increasing age, if their driving was important for others, were a volunteer, and spoke to a physician about driving, but was lower if they enjoyed driving. Our results indicate that the uptake of refresher courses is limited but is associated with potentially modifiable factors. Deepening our understanding of these facilitators and barriers will help support older adults to drive safely for longer.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".