A study of driving cessation and its association with satisfaction with life
Bibliographic record
Abstract
Older drivers are the fastest growing segment of the Canadian driving population and, as a consequence, the numbers who face the experience of stopping driving will continue to rise. A review of the literature reveals that visual, cognitive, psychomotor, medical, demographic, and social factors are associated with driving cessation and the consequences are largely negative. \nA recent cross-sectional study enabled the identification of several predictors of driving \ncessation, an assessment sensitive to the effect of driving cessation on well-being, and factors that can moderate the impact of driving cessation on subjective well-being (Kafka, 2008). The purpose of this study was to conduct a follow-up to Kafka?s (2008) study and further explore psychological variables in relation to driving cessation. We examined life purpose, life control, openness to experience, locus of control, and coping mode in participants who are still driving and those who have stopped to determine if psychological variables differ between these groups. We also examined life outcomes in relation to driving status, and the independent contribution of driving status to life outcomes. Compared to drivers, former drivers had a more external locus of control. Attrition through death, loss of contact, and refusal to participate resulted in a small sample of former drivers which may have obscured relationships in this study. Future research is required to replicate and expand on Kafka?s (2008) results.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".