Accessible and inclusive transportation for youth with disabilities: exploring innovative solutions
Bibliographic record
Abstract
Background: Although access to reliable transportation is an essential component of quality of life, young people with disabilities encounter many transportation-related obstacles.Objective: To explore solutions to the challenges that youth with disabilities encounter in accessing and navigating transportation.Methods: A nominal group technique was used in two consultation workshops (one involving rehabilitation clinicians and accessible transportation stakeholders; and one with youth with disabilities and parents). Fifteen participants across two workshops took part and prioritized their solutions and we used a comparative analysis within and between groups to explore overarching themes.Results: The workshops resulted in 122 solutions (76 from youth/parents; 46 from stakeholders). Although there was considerable overlap within the ideas generated between the groups, they each prioritized them differently. The following themes emerged across the two group’s prioritized solutions: training, funding, enhancing access, and improved efficiency.Conclusions: Our findings highlight that youth with disabilities, parents and key stakeholders offered many practical solutions for enhancing accessible transportation for youth with disabilities.Implications for RehabilitationClinicians and educators should explore different apps and transportation training programs that could help support youth with disabilities to enhance their independence and participation in the communityClinicians and educators should be involved in the development of disability awareness training programs for public transit and school bus drivers to enhance youth’s inclusion and participation in society.Clinicians, educators, youth and parents should continue to advocate for inclusive, accessible, affordable and efficient transportation for people with disabilities.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".