Holding On and Letting Go: The Perspectives of Pre-seniors and Seniors on Driving Self-Regulation in Later Life
Bibliographic record
Abstract
Although decisions related to driving are vital to well-being in later life, little is known about how aging drivers who do not experience a medical condition that requires driving cessation regulate their driving. This exploratory, qualitative study used focus groups with 79 such community-dwelling individuals to examine driving self-regulation from the perspective of pre-senior (aged 55-64) drivers, senior (aged 65 years or over) drivers, and senior ex-drivers. Themes resulting from inductive analysis addressed the importance of driving, mechanisms of self-monitoring and self-regulation, people who influenced decision making, and opinions regarding licensing regulations. A preliminary model of the process of self-regulation that highlights intrapersonal, interpersonal, and environmental influences on why, how, and when aging drivers adapt or cease driving is presented. The model identifies areas for future research to enhance understanding of this process, including the effectiveness of self-regulation. Findings suggest that increased public awareness of issues related to driving and aging could assist aging drivers, their families, and their family physicians in optimizing driving safety for this population. Since a near accident or accident was seen as the only factor that would lead many informants to stop driving and few informants planned for driving cessation, there is a need for interventions that help aging drivers make the transition to ex-driver in a timely and personally acceptable way.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".