Facilitating Aging in Place for Older Women: Assessing the Influence of Gender and Living Arrangements on Older Adult Travel Behaviour in Kingston, Ontario
Bibliographic record
Abstract
It is no secret that Canada’s population is rapidly aging, and Canadian older adults are choosing to age in the comfort of their own homes and communities. As a social determinant of health, access to transportation is a crucial component in facilitating aging in place. However, in many midsize Canadian cities, older adults are unable to meet their needs without driving. Older adults who stop driving tend to rely on partners, friends, and family to drive, leaving those who live alone without a local support network at a mobility disadvantage. Beyond age, mobility is inherently shaped by gender. Women and men do not exhibit the same travel patterns, and do not possess thesame travel needs and desires. Traditionally, transportation planning has catered to the needs of the default male commuter, with little consideration of how travel needs differ by gender, age, and living arrangements. These challenges compound for older women, who are more vulnerable than men to driving cessation and are more likely to spend their older adulthood living alone. This research employs Kingston, Ontario, as a case study to explore the impacts of gender and living arrangements on older adult travel behaviour and mobility. The analysis of household travel survey data revealed that gender and living arrangements impact the likelihood of holding a driver’s licence, trip frequency, trip purpose and travel mode choice. Semi-structured interviews highlighted the prominence of driving self-regulation behaviours among older women in Kingston, and significant concerns surrounding winter sidewalk conditions, public transit accessibility and a lack of connectivity in active transportation infrastructure. A set of recommendations for the City were developed to address the travel needs of older women in Kingston. The recommendations include improvements to existing programming and infrastructure, and a call to centre older adults, particularly older women, in policy development. This research contributes to a broader conversation on the interactions between age, gender and mobility, and the specific mobility challenges facing older adults in midsize Canadian cities.
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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 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".