Car Driving and Public Transit Use in Canadian Metropolitan Areas: Focus on Elderly and Role of Health and Social Network Factors
Bibliographic record
Abstract
Most studies analyzed the impact of decreased mobility on health and social network status, but only a few have provided evidence to understand how these latter factors could affect travel decisions or outcomes. This paper examined the linkage between people’s car driving and public transit use in Canada and their personal, health and social network characteristics, with a focus on the elderly population. The study exploits Statistics Canada’s General Social Survey (GSS-19), a unique survey with a nationally representative sample that contains questions on health, social network and transportation situation. Multilevel binary logistic regression models were estimated for the two travel modes. Results showed that regardless of age, poor health discourages both car driving and public transit use. Physical limitations that constrain mobility were found to decrease the likelihood of using public transit, a finding that was expected. However, a very interesting finding of this study is that even in the presence of physical or mental situations, mobility is still made possible through car driving. Relatedly, the study showed how important license possession and car ownership are to personal mobility and to be less dependent on other modes of transport including public transit. Findings from this study have also underlined that family network could play an important role in influencing both mobility decisions and provision. Car driving was found to be more likely when a person lives alone versus with one or more people in the household, a tendency that is stronger among the elderly than the non-elderly group. However, in the event of voluntary driving cessation, suspension of driving license, or when other means of transport would not be a convenient or feasible option, support from family members or caregivers could be critical given that, and as this study finding showed, elderly people are likely to continue to strive to maintain their driving skills even with a health condition, rather than prepare to stop driving. The size of close family networks did not show a considerable influence, but the quality of these ties (i.e. being close to family) was found relevant in public transit use. Results underlined implications to road safety, the development of alternative transport strategies and strengthening social support to help maintain mobility necessary for health and quality of life in later years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".