Enhancing Employment Access for People with Disabilities through Transportation: Insights from Workers with Disabilities, Employers, and Transportation Providers
Bibliographic record
Abstract
Transportation is integral to the employment accessibility and sustainability of people with disabilities. This study aims to identify barriers, facilitators, and solutions to commuting for people with disabilities, drawing from their perspectives as well as those of employers and transportation providers. Through semi-structured individual interviews, insights were gathered from sixteen individuals with disabilities, seven employers, two job integration agents, and four transporters. Qualitative analysis of the interview transcripts revealed factors influencing commuting, including personal attributes and environmental factors. This study underscores the significant impact of environmental factors, particularly the role of social networks and transport infrastructure in either supporting or hindering public transportation use for people with disabilities who commute to work. For example, employers’ limited awareness of their employees’ commuting challenges contrasts with their recognition of their potential role in supporting it. Training and disability awareness initiatives emerge as pivotal solutions to empower individuals within the social network, including transport personnel, fellow passengers, and employers, to facilitate public transportation use by people with disabilities for work commutes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".