Older Adults’ Motivations for Participating in a “Tune-Up” of Their Driving Skills: A Multi-Stakeholder Analysis
Bibliographic record
Abstract
Driver training has the potential to keep older adults safe behind-the-wheel for longer, yet there is limited evidence describing factors that influence their willingness to participate in training. Focus groups with community-dwelling older drivers ( n = 23; 70–90 years) and semi-structured interviews with driving instructors ( n = 6) and occupational therapists ( n = 5) were conducted to identify these factors. Qualitative descriptive analyses highlighted how self-awareness of behind-the-wheel abilities in later life can influence an older adult’s motivation to participate in driver training, as well as their willingness to discuss their behaviors. Collision-involvement and near-misses prompted participants to reflect on their driving abilities and their openness to feedback. Participants’ preferences for learning contexts that use a strengths-based approach and validate the driving experience of older drivers, while providing feedback on behind-the-wheel performance, were raised. Older driver training initiatives that consider the needs of the aging population in their design can promote road safety and community mobility.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".