COGNITIVE AND PSYCHOSOCIAL OUTCOMES OF DRIVING DIFFICULTIES IN OLDER ADULTS: A 5-YEAR STUDY
Bibliographic record
Abstract
Abstract Many older adults find it difficult to drive a car as they age. However, there are lack of studies on the outcomes of driving difficulties among older adults. The aim of this study was to examine the cognitive and psychosocial outcomes of driving difficulties in older adults. This study was a secondary data analysis using National Social Life, Health, and Aging Project Wave 2 (2010-2011) and 3 (2015-2016). This study followed 1,638 older adults that were of the age 65 and older, who had no difficulties driving a car at Wave 2. Montreal Cognitive Assessment Scale, Center for Epidemiological Studies Depression Scale, Hospital Anxiety and Depression Scale, and Perceived Social Isolation Scale were used. For data analysis, chi square tests, t-tests, and regression analysis were used. After 5 years, 11.1% of people began to have difficulties in driving a car (n=180), and 88.9% of people maintained to have no difficulties driving a car (n=1,441). Compared to people who maintained no difficulties of driving a car over time, people who began to have difficulties had more severe cognitive decline (t=4.59, p< 0.001) and more depressive symptoms over time (t=3.253, p=0.001). Univariate regression analysis also indicated that having difficulties of driving resulted in more severe cognitive decline over time (b=0.137, p< 0.001) and more depressive symptoms over time (b=0.097, p< 0.001). Driving difficulties were not related to anxiety or social isolation. As difficulties in driving are related to poor cognitive and psychological outcomes, healthcare professionals should pay more attention to people who experience driving difficulties.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".