Transportation Experiences of Autistic Young Adults: A Scoping Review and Proposal for Future Research
Bibliographic record
Abstract
Background: Many news articles, online posts, and studies related to work and school highlight barriers to community mobility for Neurodiverse individuals. This scoping review aimed to to gain a comprehensive understanding of these challenges. \n \nMethods: The search, conducted between January 2012 and January 2022, across platforms such as MEDLINE, Wiley, Taylor & Francis, Sage, and Google Scholar, yielded 56 relevant articles after eliminating duplicates and irrelevant studies. Articles fell into two main categories: those focusing on experiences and those examining interventions. \n \nResults: Results indicate a scarcity of research directly investigating the mobility experiences of Neurodiverse individuals, with the majority centered on driving difficulties among Autistic adults. Moreover, only a small number of articles explored public transit or alternative transportation methods, despite many Neurodiverse individuals primarily relying on these modes. The interventions explored varied widely, including traffic training, transit apps, monitoring tools, specialized teaching, virtual reality, and autonomous vehicles. \n \nConclusion: Overall, there is emerging literature underscores the challenges faced by Neurodiverse individuals in driving and using public transit. However, due to limited qualitative and quantitative data, the mobility needs of neurodiverse individuals remains unclear. Additionally, it seems that most research originates from the United States and Australia, leaving a significant gap in understanding the Canadian context. Consequently, further investigation is warranted, particularly with the development of the National Autism Strategy. As such, the writer proposes a two-part qualitative study on the public transportation experiences of Autistic young adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.050 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".