DRIVING CESSATION IS ASSOCIATED WITH POORER MENTAL HEALTH
Bibliographic record
Abstract
Abstract The Canadian Longitudinal Study on Aging (CLSA) is a longitudinal health study that will follow individuals aged 45 to 85 for 20 years. At baseline, participants completed measures related to driving status and mental health outcomes (e.g., Center for Epidemiologic Studies Depression Scale; CES-D). In this study we examined the associations between driving status and mental health outcomes. In the baseline sample, 1,415 participants reported being former drivers and 44,694 reported being current drivers. A greater proportion of former drivers were female, older, and urban-dwelling. Compared to current drivers, former drivers had lower levels of social support, poorer self-rated physical health, and less community participation. After controlling for these covariates as well as age and sex, former drivers had greater odds than current drivers of being classified as depressed (OR=2.48, 95% CI=2.21-2.79), and of reporting psychological distress (OR=2.22, 95% CI=1.87-2.62). Using data from former drivers only, we also examined associations between variables that contributed to driving cessation and depression symptoms. Former drivers had greater odds of being depressed if they reported feeling nervous or intimidated behind the wheel (OR=1.77, 95% CI= 1.11 - 2.80), or if they experienced difficulties with the licensing process (OR=1.62, 95% CI=1.07 - 2.54), before they stopped driving. As a next step we will search for factors that may modify the relationship between driving status and mental health. The identification of factors that modify the impact of driving cessation on mental health is critical to the development of interventions that will support smoother transitions to non-driving.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".