Local Travel Needs of Older Adults in Kingston, Ontario: An Examination of Where They are Going, How They are Getting There, and Their Quality of Life
Bibliographic record
Abstract
The western world is experiencing a rapidly aging population. In Canada, those over 65 years of age accounted for 19% of the population in 2021, and this percentage is expected to increase to nearly 26% by 2068. The World Health Organization deemed transportation one of eight domains of age-friendly cities, given its role in connecting older adults to social and civic life. Canadian cities are largely characterized by low-density built environments, which has resulted in personal automobile dependency in people of all ages. However, many older adults will eventually lose their license or choose to stop driving, which can have a negative impact on one’s mental and physical health. The purpose of this research is to understand the travel needs of older adults in a midsize city and determine how they are meeting these needs. A case study approach was utilized, focusing on Kingston, Ontario, where older adults account for 21% of the population. While the primary mode of transportation in the city is private automobile, there are taxis, buses, and the Kingston Access Bus, in addition to having a walkable downtown core, making it a suitable location for analysis. Nine semi-structured interviews were conducted with participants living in Kingston, with a range of those who remain active drivers, those who no longer drive, and those who have never driven. Based on interview results, five recommendations were developed to help improve older adult transportation needs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".