Who is willing to take transit in the future? Older adults’ perceived challenges and barriers to using public transit across Canada
Bibliographic record
Abstract
Offering public transit services that meet the needs of older adults can contribute to their independence and well-being. Based on the Aging in Place survey conducted in March 2023 (N = 3,551), this research explores the barriers preventing older Canadians (65 and older) from using public transit in their area of residence. Specifically, we use factor and cluster analysis to identify non-transit user profiles (N = 491) based on survey participants’ perceptions of public transit and their stated willingness to use it in the next year. We find four distinct groups, including transit inclined, transit is a last resort, transit is not for now, and transit averse. Each group shows variation in the extent to which they are willing to use public transit in the future. To add nuances to our segmentation findings, we conduct a thematic analysis of an open-ended question pertaining to barriers to using public transit in each region. Access to public transit, frequency, travel time, reliability, safety, infrastructure, and convenience are defined as areas for potential improvement, though the prevalence of the concerns did vary between the non-user profiles. The findings from this research can be of interest to decision-makers and public transit agencies as accounting for the heterogeneity of non-transit users can help in directing strategies promoting public transit adoption among older adults in the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".