Characterizing Older Adults' Driving Cessation and its Relationship with Life Satisfaction: Findings from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Background: Transportation is a crucial facilitator of quality of life. Most older adults drive and prefer to; however, many live long enough to experience driving cessation. Driving cessation can help improve safety for people on the road, but it can also lead to negative consequences such as depression, decreased activity participation, unfulfilled needs, and social isolation. These consequences impact quality of life. Methods: This thesis employs data from the Canadian Longitudinal Study on Aging (CLSA) to examine the reasons for driving cessation and the associated socio-demographic characteristics. Logistic regression models are used to understand the relationships between sociodemographic characteristics, driving status, life satisfaction, and a desire to participate in more activities. Results: The results confirm that the odds of not having a driver’s license significantly increase in older age. Compared to those aged 65-74, those aged 75-84 are 2.77 times (p-value = <0.001) more likely to no longer drive. People over 85 are 7.59 times (p-value = <0.001) more likely to drive no longer. Females are almost two times (OR 1.97, p-value <0.001) more likely to stop driving than males. Those partnered were less likely (OR 0.66, <0.001) to stop driving than those not. Those with lower incomes are also more likely to stop driving at an older age. Compared to those in the $50,000-$99,999 bracket, individuals with incomes between $20,000-$49,000 are almost two times (OR 1.94, p-value (<0.001) more likely to stop driving. For each unit increase in the Can-ALE scale, the odds of ceasing driving rose by 36% (OR 1.36, p-value <0.001). As expected, those with Fair/Poor health were almost 3 times (OR 2.87, <0.001) as likely to stop driving compared to those in Excellent/Very good/Good health. Conclusion: Older adults who have stopped driving are more likely to be women, have lower household incomes, and have poor health. There was not a significant relationship between driving cessation and life satisfaction
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".