Mind the Gap: Transportation Challenges for Individuals Living with Autism Spectrum Disorder
Bibliographic record
Abstract
WHY IS THIS AN IMPORTANT ISSUE?An estimated 1 in 86 children are diagnosed with Autism Spectrum Disorder (ASD)1 making it the most commonly diagnosed childhood neurological condition in Canada.2 Transportation challenges for those with ASD are a growing issue in Canada. People living with ASD3 and others who live with neurodevelopmental disability (NDD)4 rely almost exclusively on public transit and caregivers for transportation. The current transportation options are insufcient in meeting the needs of this population. WHAT DOES THE RESEARCH TELL US?Transportation is essential to promoting quality of life The transit system plays an essential role in improving quality of life for individuals with ASD and for their caregivers. However, problems with cognition, perception and communication are barriers to independence in transportation. Availability of transportation is critical to enable high levels of physical activity among those with intellectual disabilities.5 Safe and reliable transportation improves one’s ability to participate in programs that support quality of life and impacts employment, volunteering, religious participation, exercise, self-advocacy and health care for people with intellectual and developmental disabilities.6 Caregivers for those with ASD emphasize that transportation is critical to enable meaningful opportunity and community engagement in employment, education, healthcare and social pursuits.7
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".