Better Living Through Mobility: The relationship between access to transportation, well-being and disability
Bibliographic record
Abstract
Much work on making transportation accessible for people with disabilities has focused on adapting environments and infrastructure. Less work has been done on understanding the relationship between access to transportation, well-being and type of disability. The objective of this paper is to provide a better understanding of this relationship. This is achieved through a statistical analysis of Statistics Canada’s 2006 Participation and Activity Limitation Survey (PALS). The statistical analysis consists of descriptive methods and a factor and cluster statistical analysis. Results of the statistical analysis indicate that people with mental/cognitive disabilities are younger and have less income than people with sensory and physical disabilities. The statistical analysis also found that people with disabilities who have access to public transit have a higher sense of well-being. People who do not have access to public transit have a lower sense of well-being, and more so if they cannot afford personal transportation modes such as the car. This relationship between access to public transportation and well-being is more pronounced for people with mental/cognitive disabilities. The results of this research indicate that people with disabilities will have a greater quality of life if they live in areas that provide multiple transportation options. Built environments that facilitate walking and with enough density to support reliable and frequent transit options will ensure the greatest participation in society for people with disabilities. This is particularly true for people with mental/cognitive disabilities, who face an added barrier of having lower incomes and not being eligible for paratransit.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".